diff --git a/Overcooked Tutorial.ipynb b/Overcooked Tutorial.ipynb index ee485688..cd5920b1 100644 --- a/Overcooked Tutorial.ipynb +++ b/Overcooked Tutorial.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "id": "ca4bad07", "metadata": {}, "outputs": [ @@ -19,14 +19,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "Avg rew: 0.00 (std: 0.00, se: 0.00); avg len: 400.00; : 100%|██████████| 10/10 [00:00<00:00, 19.41it/s]\n", - "Avg rew: 180.00 (std: 0.00, se: 0.00); avg len: 400.00; : 100%|██████████| 1/1 [00:00<00:00, 8.01it/s]\n" + "Avg rew: 0.00 (std: 0.00, se: 0.00); avg len: 400.00; : 100%|██████████| 10/10 [00:00<00:00, 31.85it/s]\n", + "Avg rew: 200.00 (std: 0.00, se: 0.00); avg len: 400.00; : 100%|██████████| 1/1 [00:00<00:00, 14.50it/s]\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "02c455cfd0a34a6f8eaab9e8627f7697", + "model_id": "012b1f6a63184005a50e5a92d88bf9e4", "version_major": 2, "version_minor": 0 }, @@ -84,10 +84,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "7f493c88", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'tensorflow'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[2], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m layout \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcramped_room\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# any compatible layouts \u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mhuman_aware_rl\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mimitation\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbehavior_cloning_tf2\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get_bc_params, train_bc_model\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mhuman_aware_rl\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mstatic\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m CLEAN_2019_HUMAN_DATA_TRAIN\n\u001b[0;32m 5\u001b[0m params_to_override \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 6\u001b[0m \u001b[38;5;66;03m# this is the layouts where the training will happen\u001b[39;00m\n\u001b[0;32m 7\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlayouts\u001b[39m\u001b[38;5;124m\"\u001b[39m: [layout], \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mold_dynamics\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m 14\u001b[0m }\n", + "File \u001b[1;32m~\\Documents\\Master\\I_tech\\Thesis\\Code\\overcooked_ai\\src\\human_aware_rl\\imitation\\behavior_cloning_tf2.py:6\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpickle\u001b[39;00m\n\u001b[0;32m 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtf\u001b[39;00m\n\u001b[0;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mray\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrllib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpolicy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Policy \u001b[38;5;28;01mas\u001b[39;00m RllibPolicy\n\u001b[0;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m keras\n", + "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow'" + ] + } + ], "source": [ "layout = \"cramped_room\" # any compatible layouts \n", "from human_aware_rl.imitation.behavior_cloning_tf2 import get_bc_params, train_bc_model\n", @@ -128,44 +141,21 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "id": "f94ab2a8", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "(,\n", - " {'eager': True,\n", - " 'use_lstm': False,\n", - " 'cell_size': 256,\n", - " 'data_params': {'layouts': ['cramped_room'],\n", - " 'check_trajectories': False,\n", - " 'featurize_states': True,\n", - " 'data_path': '/nas/ucb/micah/overcooked_ai/src/human_aware_rl/static/human_data/cleaned/2019_hh_trials_train.pickle'},\n", - " 'mdp_params': {'layout_name': 'cramped_room', 'old_dynamics': True},\n", - " 'env_params': {'horizon': 400,\n", - " 'mlam_params': {'start_orientations': False,\n", - " 'wait_allowed': False,\n", - " 'counter_goals': [],\n", - " 'counter_drop': [],\n", - " 'counter_pickup': [],\n", - " 'same_motion_goals': True}},\n", - " 'mdp_fn_params': {},\n", - " 'mlp_params': {'num_layers': 2, 'net_arch': [64, 64]},\n", - " 'training_params': {'epochs': 10,\n", - " 'validation_split': 0.15,\n", - " 'batch_size': 64,\n", - " 'learning_rate': 0.001,\n", - " 'use_class_weights': False},\n", - " 'evaluation_params': {'ep_length': 400, 'num_games': 1, 'display': False},\n", - " 'action_shape': (6,),\n", - " 'observation_shape': (96,)})" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'tensorflow'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[3], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mhuman_aware_rl\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mimitation\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbehavior_cloning_tf2\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m load_bc_model\n\u001b[0;32m 2\u001b[0m \u001b[38;5;66;03m#this is the same path you used when training the BC agent\u001b[39;00m\n\u001b[0;32m 3\u001b[0m bc_model_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtutorial_notebook_results/BC\u001b[39m\u001b[38;5;124m\"\u001b[39m\n", + "File \u001b[1;32m~\\Documents\\Master\\I_tech\\Thesis\\Code\\overcooked_ai\\src\\human_aware_rl\\imitation\\behavior_cloning_tf2.py:6\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpickle\u001b[39;00m\n\u001b[0;32m 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtf\u001b[39;00m\n\u001b[0;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mray\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrllib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpolicy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Policy \u001b[38;5;28;01mas\u001b[39;00m RllibPolicy\n\u001b[0;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m keras\n", + "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow'" + ] } ], "source": [ @@ -186,19 +176,21 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, "id": "68c37a25", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'tensorflow'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[4], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mhuman_aware_rl\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mimitation\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbehavior_cloning_tf2\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _get_base_ae, BehaviorCloningPolicy\n\u001b[0;32m 2\u001b[0m bc_policy \u001b[38;5;241m=\u001b[39m BehaviorCloningPolicy\u001b[38;5;241m.\u001b[39mfrom_model(bc_model, bc_params, stochastic\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 3\u001b[0m \u001b[38;5;66;03m# We need the featurization function that is specifically defined for BC agent\u001b[39;00m\n\u001b[0;32m 4\u001b[0m \u001b[38;5;66;03m# The easiest way to do it is to create a base environment from the configuration and extract the featurization function\u001b[39;00m\n\u001b[0;32m 5\u001b[0m \u001b[38;5;66;03m# The environment is also needed to do evaluation\u001b[39;00m\n", + "File \u001b[1;32m~\\Documents\\Master\\I_tech\\Thesis\\Code\\overcooked_ai\\src\\human_aware_rl\\imitation\\behavior_cloning_tf2.py:6\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpickle\u001b[39;00m\n\u001b[0;32m 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtf\u001b[39;00m\n\u001b[0;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mray\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrllib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpolicy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Policy \u001b[38;5;28;01mas\u001b[39;00m RllibPolicy\n\u001b[0;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m keras\n", + "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow'" + ] } ], "source": [ @@ -252,14 +244,16 @@ "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'gym'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[5], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mhuman_aware_rl\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrllib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrllib\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m AgentPair\n\u001b[0;32m 2\u001b[0m ap_bc \u001b[38;5;241m=\u001b[39m AgentPair(bc_agent0, bc_agent1)\n\u001b[0;32m 3\u001b[0m ap_bc\n", + "File \u001b[1;32m~\\Documents\\Master\\I_tech\\Thesis\\Code\\overcooked_ai\\src\\human_aware_rl\\rllib\\rllib.py:10\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpathlib\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Path\n\u001b[0;32m 9\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mdill\u001b[39;00m\n\u001b[1;32m---> 10\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mgym\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mgymnasium\u001b[39;00m \u001b[38;5;66;03m# Things break with gymnasium because rllib can't handle it\u001b[39;00m\n\u001b[0;32m 11\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m 12\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mray\u001b[39;00m\n", + "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'gym'" + ] } ], "source": [ @@ -291,7 +285,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -323,233 +317,15 @@ "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "Avg rew: 58.00 (std: 24.41, se: 7.72); avg len: 400.00; : 100%|██████████| 10/10 [06:57<00:00, 41.80s/it]\n" + "ename": "NameError", + "evalue": "name 'ap_bc' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[7], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# ap: The AgentPair we created earlier\u001b[39;00m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;66;03m# 10: how many times we should run the evaluation since the policy is stochastic\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m trajs \u001b[38;5;241m=\u001b[39m ae\u001b[38;5;241m.\u001b[39mevaluate_agent_pair(\u001b[43map_bc\u001b[49m, \u001b[38;5;241m10\u001b[39m)\n\u001b[0;32m 4\u001b[0m trajs\n", + "\u001b[1;31mNameError\u001b[0m: name 'ap_bc' is not defined" ] - }, - { - "data": { - "text/plain": [ - "{'ep_actions': array([[((0, 0), (0, 0)), ((0, 0), (0, 0)), ((0, 1), (0, 0)), ...,\n", - " ((1, 0), (0, 0)), ((0, -1), (0, 0)), ((0, 0), (0, 0))],\n", - " [((0, 0), (0, 0)), ((0, 0), (0, 0)), ((0, 0), (0, 0)), ...,\n", - " ((0, -1), (0, 0)), ('interact', (0, 0)), ((1, 0), (0, 0))],\n", - " [((0, 0), (0, 0)), ((0, 0), (0, 0)), ((0, -1), (0, 0)), ...,\n", - " ((-1, 0), (0, 0)), ((0, 0), (0, 0)), ((0, 0), (0, 0))],\n", - " ...,\n", - " [((-1, 0), (0, 0)), ((0, 0), (0, 0)), ((0, 0), (0, 0)), ...,\n", - " ((0, 0), (0, 0)), ((0, 0), (0, 0)), ((0, 0), (0, 0))],\n", - " [((0, 0), (0, 0)), ((0, 0), (0, 0)), ((0, 0), (0, 0)), ...,\n", - " ((0, 0), (0, 0)), ((0, 0), (0, 0)), ((0, 0), (0, 0))],\n", - " [((0, 0), (0, 0)), ((0, 0), (0, 0)), ((0, 0), (0, 0)), ...,\n", - " ((0, 0), (0, 0)), ((0, 0), (0, 0)), ((0, 0), (0, 0))]],\n", - " dtype=object),\n", - " 'metadatas': {},\n", - " 'ep_infos': array([[{'agent_infos': [{'action_probs': array([[0.05297125, 0.00369196, 0.00564479, 0.01859784, 0.9077534 ,\n", - 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" [{'agent_infos': [{'action_probs': array([[0.05297125, 0.00369196, 0.00564479, 0.01859784, 0.9077534 ,\n", - " 0.01134084]], dtype=float32)}, {'action_probs': array([[0.0172119 , 0.00448586, 0.01028795, 0.02042776, 0.9353154 ,\n", - " 0.01227117]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.05297125, 0.00369196, 0.00564479, 0.01859784, 0.9077534 ,\n", - " 0.01134084]], dtype=float32)}, {'action_probs': array([[0.0172119 , 0.00448586, 0.01028795, 0.02042776, 0.9353154 ,\n", - " 0.01227117]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.05297125, 0.00369196, 0.00564479, 0.01859784, 0.9077534 ,\n", - " 0.01134084]], dtype=float32)}, {'action_probs': array([[0.0172119 , 0.00448586, 0.01028795, 0.02042776, 0.9353154 ,\n", - " 0.01227117]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " ...,\n", - " {'agent_infos': [{'action_probs': array([[0.43064207, 0.00118634, 0.04243125, 0.01245082, 0.46324122,\n", - " 0.05004829]], dtype=float32)}, {'action_probs': array([[0.07216967, 0.01146591, 0.09417965, 0.00753717, 0.7981282 ,\n", - " 0.01651939]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.035366 , 0.00080447, 0.00464188, 0.00211906, 0.30970713,\n", - " 0.64736146]], dtype=float32)}, {'action_probs': array([[0.08782221, 0.01013853, 0.09689118, 0.00953836, 0.7771887 ,\n", - " 0.018421 ]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [3, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.00814912, 0.00546082, 0.30809313, 0.02386194, 0.62155545,\n", - " 0.03287959]], dtype=float32)}, {'action_probs': array([[0.07929008, 0.02225238, 0.06561285, 0.00576123, 0.8015312 ,\n", - " 0.0255523 ]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None, 'episode': {'ep_game_stats': {'tomato_pickup': [[], []], 'useful_tomato_pickup': [[], []], 'tomato_drop': [[], []], 'useful_tomato_drop': [[], []], 'potting_tomato': [[], []], 'onion_pickup': [[10, 30, 38, 73, 107, 128, 176, 388], []], 'useful_onion_pickup': [[10, 30, 38, 73, 107, 128, 176, 388], []], 'onion_drop': [[], []], 'useful_onion_drop': [[], []], 'potting_onion': [[22, 36, 48, 100, 111, 166, 351, 398], []], 'dish_pickup': [[], [63, 96, 395]], 'useful_dish_pickup': [[], [63, 395]], 'dish_drop': [[], []], 'useful_dish_drop': [[], []], 'soup_pickup': [[], [78, 323]], 'soup_delivery': [[], [87, 347]], 'soup_drop': [[], []], 'optimal_onion_potting': [[22, 36, 48, 100, 111, 166, 351, 398], []], 'optimal_tomato_potting': [[], []], 'viable_onion_potting': [[22, 36, 48, 100, 111, 166, 351, 398], []], 'viable_tomato_potting': [[], []], 'catastrophic_onion_potting': [[], []], 'catastrophic_tomato_potting': [[], []], 'useless_onion_potting': [[], []], 'useless_tomato_potting': [[], []], 'cumulative_sparse_rewards_by_agent': array([ 0, 40]), 'cumulative_shaped_rewards_by_agent': array([24, 16])}, 'ep_sparse_r': 40, 'ep_shaped_r': 40, 'ep_sparse_r_by_agent': array([ 0, 40]), 'ep_shaped_r_by_agent': array([24, 16]), 'ep_length': 400}}],\n", - " [{'agent_infos': [{'action_probs': array([[0.05297125, 0.00369196, 0.00564479, 0.01859784, 0.9077534 ,\n", - " 0.01134084]], dtype=float32)}, {'action_probs': array([[0.0172119 , 0.00448586, 0.01028795, 0.02042776, 0.9353154 ,\n", - " 0.01227117]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.05297125, 0.00369196, 0.00564479, 0.01859784, 0.9077534 ,\n", - " 0.01134084]], dtype=float32)}, {'action_probs': array([[0.0172119 , 0.00448586, 0.01028795, 0.02042776, 0.9353154 ,\n", - " 0.01227117]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.05297125, 0.00369196, 0.00564479, 0.01859784, 0.9077534 ,\n", - " 0.01134084]], dtype=float32)}, {'action_probs': array([[0.0172119 , 0.00448586, 0.01028795, 0.02042776, 0.9353154 ,\n", - " 0.01227117]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " ...,\n", - " {'agent_infos': [{'action_probs': array([[1.9425798e-02, 2.5771598e-03, 3.3497889e-04, 1.6918801e-01,\n", - " 8.0651075e-01, 1.9632669e-03]], dtype=float32)}, {'action_probs': array([[9.9211924e-02, 1.4156876e-02, 1.3569271e-02, 1.5255912e-03,\n", - " 8.7080568e-01, 7.3057896e-04]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[1.9425798e-02, 2.5771598e-03, 3.3497889e-04, 1.6918801e-01,\n", - " 8.0651075e-01, 1.9632669e-03]], dtype=float32)}, {'action_probs': array([[9.9211924e-02, 1.4156876e-02, 1.3569271e-02, 1.5255912e-03,\n", - " 8.7080568e-01, 7.3057896e-04]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[1.9425798e-02, 2.5771598e-03, 3.3497889e-04, 1.6918801e-01,\n", - " 8.0651075e-01, 1.9632669e-03]], dtype=float32)}, {'action_probs': array([[9.9211924e-02, 1.4156876e-02, 1.3569271e-02, 1.5255912e-03,\n", - " 8.7080568e-01, 7.3057896e-04]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None, 'episode': {'ep_game_stats': {'tomato_pickup': [[], []], 'useful_tomato_pickup': [[], []], 'tomato_drop': [[], []], 'useful_tomato_drop': [[], []], 'potting_tomato': [[], []], 'onion_pickup': [[5, 12, 42, 67, 81, 101, 132, 143, 214, 228, 257, 299, 339, 365, 379], [7, 151, 299]], 'useful_onion_pickup': [[5, 12, 42, 67, 81, 101, 132, 143, 214, 228, 257, 299, 365, 379], [7, 151, 299]], 'onion_drop': [[], []], 'useful_onion_drop': [[], []], 'potting_onion': [[9, 28, 63, 77, 94, 124, 137, 154, 222, 238, 294, 322, 359, 372], [20, 196, 307]], 'dish_pickup': [[161], [26, 93, 224, 342, 368]], 'useful_dish_pickup': [[161], [26, 93, 224, 342, 368]], 'dish_drop': [[], []], 'useful_dish_drop': [[], []], 'soup_pickup': [[185], [49, 115, 264, 350]], 'soup_delivery': [[], [62, 126, 280, 358]], 'soup_drop': [[190], []], 'optimal_onion_potting': [[9, 28, 63, 77, 94, 124, 137, 154, 222, 238, 294, 322, 359, 372], [20, 196, 307]], 'optimal_tomato_potting': [[], []], 'viable_onion_potting': [[9, 28, 63, 77, 94, 124, 137, 154, 222, 238, 294, 322, 359, 372], [20, 196, 307]], 'viable_tomato_potting': [[], []], 'catastrophic_onion_potting': [[], []], 'catastrophic_tomato_potting': [[], []], 'useless_onion_potting': [[], []], 'useless_tomato_potting': [[], []], 'cumulative_sparse_rewards_by_agent': array([ 0, 80]), 'cumulative_shaped_rewards_by_agent': array([50, 44])}, 'ep_sparse_r': 80, 'ep_shaped_r': 94, 'ep_sparse_r_by_agent': array([ 0, 80]), 'ep_shaped_r_by_agent': array([50, 44]), 'ep_length': 400}}],\n", - " ...,\n", - " [{'agent_infos': [{'action_probs': array([[0.05297125, 0.00369196, 0.00564479, 0.01859784, 0.9077534 ,\n", - " 0.01134084]], dtype=float32)}, {'action_probs': array([[0.0172119 , 0.00448586, 0.01028795, 0.02042776, 0.9353154 ,\n", - " 0.01227117]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.15208364, 0.02959962, 0.03100314, 0.09762987, 0.6794261 ,\n", - " 0.01025762]], dtype=float32)}, {'action_probs': array([[0.01076664, 0.00366979, 0.01067814, 0.01863392, 0.94092256,\n", - " 0.01532892]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.15208364, 0.02959962, 0.03100314, 0.09762987, 0.6794261 ,\n", - " 0.01025762]], dtype=float32)}, {'action_probs': array([[0.01076664, 0.00366979, 0.01067814, 0.01863392, 0.94092256,\n", - " 0.01532892]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " ...,\n", - " {'agent_infos': [{'action_probs': array([[0.02323768, 0.01111274, 0.00306184, 0.0065601 , 0.95041126,\n", - " 0.00561639]], dtype=float32)}, {'action_probs': array([[0.01019176, 0.00143141, 0.0016537 , 0.24706495, 0.72344553,\n", - " 0.01621261]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.02323768, 0.01111274, 0.00306184, 0.0065601 , 0.95041126,\n", - " 0.00561639]], dtype=float32)}, {'action_probs': array([[0.01019176, 0.00143141, 0.0016537 , 0.24706495, 0.72344553,\n", - " 0.01621261]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.02323768, 0.01111274, 0.00306184, 0.0065601 , 0.95041126,\n", - " 0.00561639]], dtype=float32)}, {'action_probs': array([[0.01019176, 0.00143141, 0.0016537 , 0.24706495, 0.72344553,\n", - " 0.01621261]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None, 'episode': {'ep_game_stats': {'tomato_pickup': [[], []], 'useful_tomato_pickup': [[], []], 'tomato_drop': [[], []], 'useful_tomato_drop': [[], []], 'potting_tomato': [[], []], 'onion_pickup': [[], [59, 84, 94, 105, 146, 160, 190, 211, 224, 252, 266, 308, 337, 364, 392]], 'useful_onion_pickup': [[], [59, 84, 94, 105, 146, 160, 190, 211, 224, 252, 266, 308, 337, 364, 392]], 'onion_drop': [[], []], 'useful_onion_drop': [[], []], 'potting_onion': [[], [75, 90, 98, 135, 152, 165, 207, 216, 227, 261, 272, 326, 361, 371]], 'dish_pickup': [[104, 159, 237, 284, 368], []], 'useful_dish_pickup': [[104, 159, 237, 284, 368], []], 'dish_drop': [[], []], 'useful_dish_drop': [[], []], 'soup_pickup': [[122, 189, 250, 348], [286]], 'soup_delivery': [[201, 257], [296]], 'soup_drop': [[130, 362], []], 'optimal_onion_potting': [[], [75, 90, 98, 135, 152, 165, 207, 216, 227, 261, 272, 326, 361, 371]], 'optimal_tomato_potting': [[], []], 'viable_onion_potting': [[], [75, 90, 98, 135, 152, 165, 207, 216, 227, 261, 272, 326, 361, 371]], 'viable_tomato_potting': [[], []], 'catastrophic_onion_potting': [[], []], 'catastrophic_tomato_potting': [[], []], 'useless_onion_potting': [[], []], 'useless_tomato_potting': [[], []], 'cumulative_sparse_rewards_by_agent': array([40, 20]), 'cumulative_shaped_rewards_by_agent': array([35, 42])}, 'ep_sparse_r': 60, 'ep_shaped_r': 77, 'ep_sparse_r_by_agent': array([40, 20]), 'ep_shaped_r_by_agent': array([35, 42]), 'ep_length': 400}}],\n", - " [{'agent_infos': [{'action_probs': array([[0.05297125, 0.00369196, 0.00564479, 0.01859784, 0.9077534 ,\n", - " 0.01134084]], dtype=float32)}, {'action_probs': array([[0.0172119 , 0.00448586, 0.01028795, 0.02042776, 0.9353154 ,\n", - " 0.01227117]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.05297125, 0.00369196, 0.00564479, 0.01859784, 0.9077534 ,\n", - " 0.01134084]], dtype=float32)}, {'action_probs': array([[0.0172119 , 0.00448586, 0.01028795, 0.02042776, 0.9353154 ,\n", - " 0.01227117]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.05297125, 0.00369196, 0.00564479, 0.01859784, 0.9077534 ,\n", - " 0.01134084]], dtype=float32)}, {'action_probs': array([[0.0172119 , 0.00448586, 0.01028795, 0.02042776, 0.9353154 ,\n", - " 0.01227117]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " ...,\n", - " {'agent_infos': [{'action_probs': array([[0.01547191, 0.00609912, 0.01313627, 0.04950164, 0.81513834,\n", - " 0.10065278]], dtype=float32)}, {'action_probs': array([[0.10248725, 0.00658033, 0.1326387 , 0.01534621, 0.74053526,\n", - " 0.00241225]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.01547191, 0.00609912, 0.01313627, 0.04950164, 0.81513834,\n", - " 0.10065278]], dtype=float32)}, {'action_probs': array([[0.10248725, 0.00658033, 0.1326387 , 0.01534621, 0.74053526,\n", - " 0.00241225]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.01547191, 0.00609912, 0.01313627, 0.04950164, 0.81513834,\n", - " 0.10065278]], dtype=float32)}, {'action_probs': array([[0.10248725, 0.00658033, 0.1326387 , 0.01534621, 0.74053526,\n", - " 0.00241225]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None, 'episode': {'ep_game_stats': {'tomato_pickup': [[], []], 'useful_tomato_pickup': [[], []], 'tomato_drop': [[], []], 'useful_tomato_drop': [[], []], 'potting_tomato': [[], []], 'onion_pickup': [[34, 53, 71, 99, 140, 177, 210, 226, 269, 280, 292, 303, 337], [11]], 'useful_onion_pickup': [[34, 53, 71, 99, 140, 177, 210, 226, 269, 280, 292, 303, 337], [11]], 'onion_drop': [[], []], 'useful_onion_drop': [[], []], 'potting_onion': [[41, 64, 94, 105, 171, 207, 220, 233, 276, 288, 298, 328, 355], [29]], 'dish_pickup': [[], [42, 104, 210, 300, 343]], 'useful_dish_pickup': [[], [42, 104, 210, 300, 343]], 'dish_drop': [[], []], 'useful_dish_drop': [[], []], 'soup_pickup': [[], [87, 192, 268, 318]], 'soup_delivery': [[], [90, 196, 289, 329]], 'soup_drop': [[], []], 'optimal_onion_potting': [[41, 64, 94, 105, 171, 207, 220, 233, 276, 288, 298, 328, 355], [29]], 'optimal_tomato_potting': [[], []], 'viable_onion_potting': [[41, 64, 94, 105, 171, 207, 220, 233, 276, 288, 298, 328, 355], [29]], 'viable_tomato_potting': [[], []], 'catastrophic_onion_potting': [[], []], 'catastrophic_tomato_potting': [[], []], 'useless_onion_potting': [[], []], 'useless_tomato_potting': [[], []], 'cumulative_sparse_rewards_by_agent': array([ 0, 80]), 'cumulative_shaped_rewards_by_agent': array([39, 38])}, 'ep_sparse_r': 80, 'ep_shaped_r': 77, 'ep_sparse_r_by_agent': array([ 0, 80]), 'ep_shaped_r_by_agent': array([39, 38]), 'ep_length': 400}}],\n", - " [{'agent_infos': [{'action_probs': array([[0.05297125, 0.00369196, 0.00564479, 0.01859784, 0.9077534 ,\n", - " 0.01134084]], dtype=float32)}, {'action_probs': array([[0.0172119 , 0.00448586, 0.01028795, 0.02042776, 0.9353154 ,\n", - " 0.01227117]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.05297125, 0.00369196, 0.00564479, 0.01859784, 0.9077534 ,\n", - " 0.01134084]], dtype=float32)}, {'action_probs': array([[0.0172119 , 0.00448586, 0.01028795, 0.02042776, 0.9353154 ,\n", - " 0.01227117]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.05297125, 0.00369196, 0.00564479, 0.01859784, 0.9077534 ,\n", - " 0.01134084]], dtype=float32)}, {'action_probs': array([[0.0172119 , 0.00448586, 0.01028795, 0.02042776, 0.9353154 ,\n", - " 0.01227117]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " ...,\n", - " {'agent_infos': [{'action_probs': array([[0.02207099, 0.10103653, 0.00525927, 0.08316644, 0.66694003,\n", - " 0.12152667]], dtype=float32)}, {'action_probs': array([[0.0107964 , 0.00767973, 0.00561914, 0.05614307, 0.90523726,\n", - " 0.01452435]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.02207099, 0.10103653, 0.00525927, 0.08316644, 0.66694003,\n", - " 0.12152667]], dtype=float32)}, {'action_probs': array([[0.0107964 , 0.00767973, 0.00561914, 0.05614307, 0.90523726,\n", - " 0.01452435]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.02207099, 0.10103653, 0.00525927, 0.08316644, 0.66694003,\n", - " 0.12152667]], dtype=float32)}, {'action_probs': array([[0.0107964 , 0.00767973, 0.00561914, 0.05614307, 0.90523726,\n", - " 0.01452435]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None, 'episode': {'ep_game_stats': {'tomato_pickup': [[], []], 'useful_tomato_pickup': [[], []], 'tomato_drop': [[], []], 'useful_tomato_drop': [[], []], 'potting_tomato': [[], []], 'onion_pickup': [[27, 229, 306, 315, 317, 377, 383], [30, 58, 75, 157, 175, 211]], 'useful_onion_pickup': [[27], [30, 58, 75, 157, 175]], 'onion_drop': [[301, 308, 316, 370, 382, 392], []], 'useful_onion_drop': [[301, 308, 316, 370, 382, 392], []], 'potting_onion': [[33], [49, 73, 136, 161, 183]], 'dish_pickup': [[52], []], 'useful_dish_pickup': [[52], []], 'dish_drop': [[], []], 'useful_dish_drop': [[], []], 'soup_pickup': [[110], []], 'soup_delivery': [[129], []], 'soup_drop': [[], []], 'optimal_onion_potting': [[33], [49, 73, 136, 161, 183]], 'optimal_tomato_potting': [[], []], 'viable_onion_potting': [[33], [49, 73, 136, 161, 183]], 'viable_tomato_potting': [[], []], 'catastrophic_onion_potting': [[], []], 'catastrophic_tomato_potting': [[], []], 'useless_onion_potting': [[], []], 'useless_tomato_potting': [[], []], 'cumulative_sparse_rewards_by_agent': array([20, 0]), 'cumulative_shaped_rewards_by_agent': array([11, 15])}, 'ep_sparse_r': 20, 'ep_shaped_r': 26, 'ep_sparse_r_by_agent': array([20, 0]), 'ep_shaped_r_by_agent': array([11, 15]), 'ep_length': 400}}]],\n", - " dtype=object),\n", - " 'ep_dones': array([[False, False, False, ..., False, False, True],\n", - " [False, False, False, ..., False, False, True],\n", - " [False, False, False, ..., False, False, True],\n", - " ...,\n", - " [False, False, False, ..., False, False, True],\n", - " [False, False, False, ..., False, False, True],\n", - " [False, False, False, ..., False, False, True]], dtype=object),\n", - " 'ep_returns': array([80, 40, 80, 40, 80, 80, 20, 60, 80, 20]),\n", - " 'env_params': array([{'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", - " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", - " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", - " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", - " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", - " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", - " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", - " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", - " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", - " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1}],\n", - " dtype=object),\n", - " 'ep_lengths': array([400, 400, 400, 400, 400, 400, 400, 400, 400, 400]),\n", - " 'mdp_params': array([{'layout_name': 'cramped_room', 'terrain': [['X', 'X', 'P', 'X', 'X'], ['O', ' ', ' ', ' ', 'O'], ['X', ' ', ' ', ' ', 'X'], ['X', 'D', 'X', 'S', 'X']], 'start_player_positions': [(1, 2), (3, 1)], 'start_bonus_orders': [], 'rew_shaping_params': {'PLACEMENT_IN_POT_REW': 3, 'DISH_PICKUP_REWARD': 3, 'SOUP_PICKUP_REWARD': 5, 'DISH_DISP_DISTANCE_REW': 0, 'POT_DISTANCE_REW': 0, 'SOUP_DISTANCE_REW': 0}, 'start_all_orders': [{'ingredients': ['onion', 'onion', 'onion']}]},\n", - " {'layout_name': 'cramped_room', 'terrain': [['X', 'X', 'P', 'X', 'X'], ['O', ' ', ' ', ' ', 'O'], ['X', ' ', ' ', ' ', 'X'], ['X', 'D', 'X', 'S', 'X']], 'start_player_positions': [(1, 2), (3, 1)], 'start_bonus_orders': [], 'rew_shaping_params': {'PLACEMENT_IN_POT_REW': 3, 'DISH_PICKUP_REWARD': 3, 'SOUP_PICKUP_REWARD': 5, 'DISH_DISP_DISTANCE_REW': 0, 'POT_DISTANCE_REW': 0, 'SOUP_DISTANCE_REW': 0}, 'start_all_orders': [{'ingredients': ['onion', 'onion', 'onion']}]},\n", - " {'layout_name': 'cramped_room', 'terrain': [['X', 'X', 'P', 'X', 'X'], ['O', ' ', ' ', ' ', 'O'], ['X', ' ', ' ', ' ', 'X'], ['X', 'D', 'X', 'S', 'X']], 'start_player_positions': [(1, 2), (3, 1)], 'start_bonus_orders': [], 'rew_shaping_params': {'PLACEMENT_IN_POT_REW': 3, 'DISH_PICKUP_REWARD': 3, 'SOUP_PICKUP_REWARD': 5, 'DISH_DISP_DISTANCE_REW': 0, 'POT_DISTANCE_REW': 0, 'SOUP_DISTANCE_REW': 0}, 'start_all_orders': [{'ingredients': ['onion', 'onion', 'onion']}]},\n", - " {'layout_name': 'cramped_room', 'terrain': [['X', 'X', 'P', 'X', 'X'], ['O', ' ', ' ', ' ', 'O'], ['X', ' ', ' ', ' ', 'X'], ['X', 'D', 'X', 'S', 'X']], 'start_player_positions': [(1, 2), (3, 1)], 'start_bonus_orders': [], 'rew_shaping_params': {'PLACEMENT_IN_POT_REW': 3, 'DISH_PICKUP_REWARD': 3, 'SOUP_PICKUP_REWARD': 5, 'DISH_DISP_DISTANCE_REW': 0, 'POT_DISTANCE_REW': 0, 'SOUP_DISTANCE_REW': 0}, 'start_all_orders': [{'ingredients': ['onion', 'onion', 'onion']}]},\n", - " {'layout_name': 'cramped_room', 'terrain': [['X', 'X', 'P', 'X', 'X'], ['O', ' ', ' ', ' ', 'O'], ['X', ' ', ' ', ' ', 'X'], ['X', 'D', 'X', 'S', 'X']], 'start_player_positions': [(1, 2), (3, 1)], 'start_bonus_orders': [], 'rew_shaping_params': {'PLACEMENT_IN_POT_REW': 3, 'DISH_PICKUP_REWARD': 3, 'SOUP_PICKUP_REWARD': 5, 'DISH_DISP_DISTANCE_REW': 0, 'POT_DISTANCE_REW': 0, 'SOUP_DISTANCE_REW': 0}, 'start_all_orders': [{'ingredients': ['onion', 'onion', 'onion']}]},\n", - " {'layout_name': 'cramped_room', 'terrain': [['X', 'X', 'P', 'X', 'X'], ['O', ' ', ' ', ' ', 'O'], ['X', ' ', ' ', ' ', 'X'], ['X', 'D', 'X', 'S', 'X']], 'start_player_positions': [(1, 2), (3, 1)], 'start_bonus_orders': [], 'rew_shaping_params': {'PLACEMENT_IN_POT_REW': 3, 'DISH_PICKUP_REWARD': 3, 'SOUP_PICKUP_REWARD': 5, 'DISH_DISP_DISTANCE_REW': 0, 'POT_DISTANCE_REW': 0, 'SOUP_DISTANCE_REW': 0}, 'start_all_orders': [{'ingredients': ['onion', 'onion', 'onion']}]},\n", - " {'layout_name': 'cramped_room', 'terrain': [['X', 'X', 'P', 'X', 'X'], ['O', ' ', ' ', ' ', 'O'], ['X', ' ', ' ', ' ', 'X'], ['X', 'D', 'X', 'S', 'X']], 'start_player_positions': [(1, 2), (3, 1)], 'start_bonus_orders': [], 'rew_shaping_params': {'PLACEMENT_IN_POT_REW': 3, 'DISH_PICKUP_REWARD': 3, 'SOUP_PICKUP_REWARD': 5, 'DISH_DISP_DISTANCE_REW': 0, 'POT_DISTANCE_REW': 0, 'SOUP_DISTANCE_REW': 0}, 'start_all_orders': [{'ingredients': ['onion', 'onion', 'onion']}]},\n", - " {'layout_name': 'cramped_room', 'terrain': [['X', 'X', 'P', 'X', 'X'], ['O', ' ', ' ', ' ', 'O'], ['X', ' ', ' ', ' ', 'X'], ['X', 'D', 'X', 'S', 'X']], 'start_player_positions': [(1, 2), (3, 1)], 'start_bonus_orders': [], 'rew_shaping_params': {'PLACEMENT_IN_POT_REW': 3, 'DISH_PICKUP_REWARD': 3, 'SOUP_PICKUP_REWARD': 5, 'DISH_DISP_DISTANCE_REW': 0, 'POT_DISTANCE_REW': 0, 'SOUP_DISTANCE_REW': 0}, 'start_all_orders': [{'ingredients': ['onion', 'onion', 'onion']}]},\n", - " {'layout_name': 'cramped_room', 'terrain': [['X', 'X', 'P', 'X', 'X'], ['O', ' ', ' ', ' ', 'O'], ['X', ' ', ' ', ' ', 'X'], ['X', 'D', 'X', 'S', 'X']], 'start_player_positions': [(1, 2), (3, 1)], 'start_bonus_orders': [], 'rew_shaping_params': {'PLACEMENT_IN_POT_REW': 3, 'DISH_PICKUP_REWARD': 3, 'SOUP_PICKUP_REWARD': 5, 'DISH_DISP_DISTANCE_REW': 0, 'POT_DISTANCE_REW': 0, 'SOUP_DISTANCE_REW': 0}, 'start_all_orders': [{'ingredients': ['onion', 'onion', 'onion']}]},\n", - " {'layout_name': 'cramped_room', 'terrain': [['X', 'X', 'P', 'X', 'X'], ['O', ' ', ' ', ' ', 'O'], ['X', ' ', ' ', ' ', 'X'], ['X', 'D', 'X', 'S', 'X']], 'start_player_positions': [(1, 2), (3, 1)], 'start_bonus_orders': [], 'rew_shaping_params': {'PLACEMENT_IN_POT_REW': 3, 'DISH_PICKUP_REWARD': 3, 'SOUP_PICKUP_REWARD': 5, 'DISH_DISP_DISTANCE_REW': 0, 'POT_DISTANCE_REW': 0, 'SOUP_DISTANCE_REW': 0}, 'start_all_orders': [{'ingredients': ['onion', 'onion', 'onion']}]}],\n", - " dtype=object),\n", - " 'ep_rewards': array([[0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " ...,\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0]], dtype=object),\n", - " 'ep_states': array([[,\n", - " ,\n", - " ,\n", - " ...,\n", - " ,\n", - " ,\n", - " ],\n", - " [,\n", - " ,\n", - " ,\n", - " ...,\n", - " ,\n", - " ,\n", - " ],\n", - " [,\n", - " ,\n", - " ,\n", - " ...,\n", - " ,\n", - " ,\n", - " ],\n", - " ...,\n", - " [,\n", - " ,\n", - " ,\n", - " ...,\n", - " ,\n", - " ,\n", - " ],\n", - " [,\n", - " ,\n", - " ,\n", - " ...,\n", - " ,\n", - " ,\n", - " ],\n", - " [,\n", - " ,\n", - " ,\n", - " ...,\n", - " ,\n", - " ,\n", - " ]],\n", - " dtype=object)}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -613,7 +389,22 @@ "execution_count": null, "id": "464d0c84", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3cab20bc2bce40519468eda497a67aae", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(IntSlider(value=0, description='timestep', max=399), Output()), _dom_classes=('widget-in…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from overcooked_ai_py.visualization.state_visualizer import StateVisualizer\n", "StateVisualizer().display_rendered_trajectory(trajs, ipython_display=True)" @@ -630,7 +421,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "overcooked_ai (3.10.0)", "language": "python", "name": "python3" }, @@ -644,7 +435,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.16" + "version": "3.10.0" } }, "nbformat": 4, diff --git a/Sybe/TODO.txt b/Sybe/TODO.txt new file mode 100644 index 00000000..8bb4badc --- /dev/null +++ b/Sybe/TODO.txt @@ -0,0 +1,26 @@ + + + +### DONE! ### +- Fixed Python version conflict +- Repaired pip, created virtual environment +- Fixed dependency hell with mismatching versions of libraries + +- Figured out how to generate game data with 2 random agents +- Implemented manual stitching of pngs to create a gif +- Created an interactive viewer in a pygame environment +- Added HUD to viewer, improved UX with tap/hold of buttons. +- Restructured code; created file hierarchy +- Created next run function and improved pathing + +### Issues: ### +- (base) env? check correctness +- + +### TODO: ### +- +- Replace random agents with reinforcement learning agents +- Train on the JaxMARL server +- Save trajectory instead of gif + + diff --git a/Sybe/archive/make_gif.py b/Sybe/archive/make_gif.py new file mode 100644 index 00000000..f11421aa --- /dev/null +++ b/Sybe/archive/make_gif.py @@ -0,0 +1,189 @@ +import os +import time +import pygame +import imageio +from overcooked_ai_py.agents.agent import AgentPair, RandomAgent +from overcooked_ai_py.agents.benchmarking import AgentEvaluator +from overcooked_ai_py.visualization.state_visualizer import StateVisualizer + +# --- Configuration --- +LAYOUT_NAME = "cramped_room" +HORIZON = 400 +FPS = 30 +OUTPUT_DIR = os.path.join("Sybe", "experiments", "game1") + +# --- Utility Functions --- + +def generate_game_data(layout_name, horizon): + """Runs a single simulation with random agents and returns the history data.""" + print(f"Simulating game on '{layout_name}' for {horizon} steps...") + + # 1. Setup Evaluator + agent_evaluator = AgentEvaluator.from_layout_name( + mdp_params={"layout_name": layout_name}, + env_params={"horizon": horizon} + ) + + # 2. Run Game + agent_pair = AgentPair(RandomAgent(), RandomAgent()) + # The full history is a dict of lists containing states, actions, rewards, etc. + game_history = agent_evaluator.evaluate_agent_pair(agent_pair, num_games=1) + + # 3. Extract Core Data (assuming only one game was run) + state_list = game_history["ep_states"][0] + grid_matrix = agent_evaluator.env.mdp.terrain_mtx + + return game_history, state_list, grid_matrix, agent_evaluator + +def stitch_gif(game_history, output_dir, gif_name="interactive_replay.gif", gif_fps=10): + """Saves individual frames to a directory and stitches them into a GIF.""" + + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + print(f"Rendering frames to {output_dir}...") + + # 1. Render all frames to PNG files + StateVisualizer().display_rendered_trajectory( + trajectories=game_history, + img_directory_path=output_dir, + ipython_display=False + ) + + # 2. Stitch images into a GIF + print("Stitching frames into GIF...") + images = [] + + # Get all PNG filenames and sort them numerically + filenames = sorted( + [f for f in os.listdir(output_dir) if f.endswith('.png')], + key=lambda x: int(''.join(filter(str.isdigit, x))) + ) + + for filename in filenames: + file_path = os.path.join(output_dir, filename) + images.append(imageio.imread(file_path)) + + output_path = os.path.join(output_dir, gif_name) + imageio.mimsave(output_path, images, duration=(1/gif_fps), loop=0) + + print(f"GIF successfully saved to: {output_path}") + +def run_interactive_viewer(game_history, state_list, grid_matrix, mdp_env, screen_fps=30): + """Opens a Pygame window to interactively replay the game, including HUD.""" + + pygame.init() + + # --- KEY REPEAT SETUP --- + # Argument 1: Delay before repeat starts (500ms = 0.5 seconds) + # Argument 2: Interval between repeats (30ms = ~33 steps per second) + pygame.key.set_repeat(500, 30) + + viz = StateVisualizer() + font = pygame.font.SysFont(None, 24) + + rewards_list = game_history["ep_rewards"][0] + + def get_game_surface(t): + return viz.render_state(state_list[t], grid_matrix) + + def draw_hud(surface, timestep): + total_score = int(game_history["ep_returns"][0]) + + if timestep == 0: + current_score = 0 + else: + current_score = int(sum(rewards_list[:timestep])) + + max_time = mdp_env.horizon + time_left = max(0, max_time - timestep) + + # Text Rendering + text_time = font.render(f"TIME LEFT: {time_left}", True, (255, 255, 255)) + text_score = font.render(f"SCORE: {current_score} / {total_score}", True, (255, 255, 255)) + + surface.blit(text_time, (20, 10)) + surface.blit(text_score, (20, 35)) + + # Setup Window + game_surface = get_game_surface(0) + game_width, game_height = game_surface.get_size() + + HUD_HEIGHT = 60 + screen = pygame.display.set_mode((game_width, game_height + HUD_HEIGHT)) + clock = pygame.time.Clock() + + # State variables + running = True + current_timestep = 0 + playing = False + + BG_COLOR = (153, 102, 0) + + print("\n--- INTERACTIVE CONTROLS ---") + print("Tap/Hold Right : Step Forward") + print("Tap/Hold Left : Step Backward") + print("Space : Play / Pause") + print("Esc : Quit") + print("----------------------------") + + while running: + # 1. Event Handling (Handles Taps AND Holds via set_repeat) + for event in pygame.event.get(): + if event.type == pygame.QUIT: + running = False + elif event.type == pygame.KEYDOWN: + if event.key == pygame.K_ESCAPE: + running = False + elif event.key == pygame.K_RIGHT: + current_timestep = min(current_timestep + 1, len(state_list) - 1) + playing = False # Stop auto-play on manual input + elif event.key == pygame.K_LEFT: + current_timestep = max(current_timestep - 1, 0) + playing = False + elif event.key == pygame.K_SPACE: + playing = not playing + + # 2. Auto-Play Logic + if playing: + if current_timestep < len(state_list) - 1: + current_timestep += 1 + # We use clock.tick to control speed, so no sleep needed here + else: + playing = False + + # 3. Drawing + screen.fill(BG_COLOR) + + draw_hud(screen, current_timestep) + + game_surface = get_game_surface(current_timestep) + screen.blit(game_surface, (0, HUD_HEIGHT)) + + pygame.display.set_caption(f"Replay - Step {current_timestep}/{len(state_list)-1} {'[PLAYING]' if playing else '[PAUSED]'}") + pygame.display.flip() + + clock.tick(screen_fps) + + pygame.quit() + + +# --- Main Execution Block --- +if __name__ == "__main__": + + # 1. Run Simulation + game_history, state_list, grid_matrix, agent_evaluator = generate_game_data( + layout_name=LAYOUT_NAME, + horizon=HORIZON + ) + + # 2. Save GIF (Uncomment this line to generate the GIF) + # stitch_gif(game_history, output_dir=OUTPUT_DIR, gif_fps=10) + + # 3. Open Interactive Viewer + run_interactive_viewer( + game_history=game_history, + state_list=state_list, + grid_matrix=grid_matrix, + mdp_env=agent_evaluator.env + ) \ No newline at end of file diff --git a/Sybe/archive/visualise.py b/Sybe/archive/visualise.py new file mode 100644 index 00000000..346acb73 --- /dev/null +++ b/Sybe/archive/visualise.py @@ -0,0 +1,13 @@ +from overcooked_ai_py.mdp.overcooked_mdp import OvercookedGridworld +from overcooked_ai_py.mdp.overcooked_env import OvercookedEnv + +# Load a classic layout (e.g., "cramped_room") +# Other layouts include: "asymmetric_advantages", "coordination_ring", "forced_coordination", "counter_circuit_o_1order" +mdp = OvercookedGridworld.from_layout_name("cramped_room") +env = OvercookedEnv.from_mdp(mdp, horizon=400) + +# Reset the environment +env.reset() + +# Print the text-based representation of the grid +print(env) \ No newline at end of file diff --git a/Sybe/archive/viz_as_png.py b/Sybe/archive/viz_as_png.py new file mode 100644 index 00000000..3cf74de3 --- /dev/null +++ b/Sybe/archive/viz_as_png.py @@ -0,0 +1,46 @@ +import os +from overcooked_ai_py.mdp.overcooked_mdp import OvercookedGridworld +from overcooked_ai_py.mdp.overcooked_env import OvercookedEnv +from overcooked_ai_py.visualization.state_visualizer import StateVisualizer +from overcooked_ai_py.mdp.actions import Action +import random + +# 1. Setup the environment +layout_name = "cramped_room" +mdp = OvercookedGridworld.from_layout_name(layout_name) +env = OvercookedEnv.from_mdp(mdp, horizon=400) +env.reset() + +# 2. Get a state (just stepping randomly for demonstration) +# Taking a random action for agent 0 and agent 1 +action_0 = random.choice(Action.ALL_ACTIONS) +action_1 = random.choice(Action.ALL_ACTIONS) +state, reward, done, info = env.step((action_0, action_1)) + +print("actions taken:", action_0, action_1) +print("Current State after one random step:") +print(state) +print("Reward:", reward) +print("Done:", done) +print("Info:", info) + +# 3. Create the visualizer +visualizer = StateVisualizer() + +# 4. Render and Save +# You need the 'grid' from the mdp to tell the visualizer where the walls/counters are +img_dir = "Sybe/replays" +if not os.path.exists(img_dir): + os.makedirs(img_dir) + +img_path = os.path.join(img_dir, "test_state.png") + +print(f"Saving image to {img_path}...") +visualizer.display_rendered_state( + state=state, + grid=mdp.terrain_mtx, + img_path=img_path +) +print("Done!") + +StateVisualizer().display_rendered_trajectory(trajs2, ipython_display=True) \ No newline at end of file diff --git a/Sybe/config.py b/Sybe/config.py new file mode 100644 index 00000000..6c92b702 --- /dev/null +++ b/Sybe/config.py @@ -0,0 +1,26 @@ +"""Central place for constants and paths.""" +from pathlib import Path + +LAYOUT_NAME = "cramped_room" +HORIZON = 400 + +VIEWER_FPS = 30 +GIF_FPS = 10 +HUD_HEIGHT = 60 +BG_COLOR = (153, 102, 0) + +EXPERIMENTS = [ + "random", + "ddqn_baseline", + "independent_curiosity", +] + +# AGENT_PAIRS = { +# "random": ("random", "random"), +# "ddqn_baseline": ("ddqn", "ddqn"), +# "independent_curiosity": ("curiosity", "curiosity"), +# } + +# Paths are relative to this file, so scripts work from any working directory +BASE_DIR = Path(__file__).resolve().parent +# OUTPUT_DIR = BASE_DIR / "experiments" / "random" / "run_000001" / "renders" / "episode_000004" \ No newline at end of file diff --git a/Sybe/experiments/random/run_0001/episode_000001/frames/_trajectory.gif b/Sybe/experiments/random/run_0001/episode_000001/frames/_trajectory.gif new file mode 100644 index 00000000..889f9d2d Binary files /dev/null and b/Sybe/experiments/random/run_0001/episode_000001/frames/_trajectory.gif differ diff --git a/Sybe/experiments/random/run_0001/episode_000002/frames/_trajectory.gif b/Sybe/experiments/random/run_0001/episode_000002/frames/_trajectory.gif new file mode 100644 index 00000000..4fe7dd61 Binary files /dev/null and b/Sybe/experiments/random/run_0001/episode_000002/frames/_trajectory.gif differ diff --git a/Sybe/main.py b/Sybe/main.py new file mode 100644 index 00000000..c96d0cd6 --- /dev/null +++ b/Sybe/main.py @@ -0,0 +1,58 @@ +"""Entry point: simulate a game. + +Run from anywhere: + python Sybe/main.py # simulate a game + python Sybe/main.py --save_frames # save frames + python Sybe/main.py --viewer # show pygame viewer +""" +import argparse + +from overcooked_irl.agent_factory import create_agent_pair +import config +from overcooked_irl.export import save_frames, save_gif +from overcooked_irl.simulation import random_agent_pair, run_game +from overcooked_irl.viewer import run_viewer +from overcooked_irl.paths import next_episode_dir, next_run_dir +from overcooked_ai_py.visualization.state_visualizer import StateVisualizer # TODO Remove StateVisualizer logic from main? + +def parse_args(): + p = argparse.ArgumentParser(description="Overcooked simulation + visualization") + p.add_argument("--layout", default=config.LAYOUT_NAME) + p.add_argument("--horizon", type=int, default=config.HORIZON) + p.add_argument( + "--experiment", + choices=config.EXPERIMENTS, + default="random", + help="Experiment configuration", + ) + p.add_argument("--save_frames", action="store_true", help="saves images of every timestep") + p.add_argument("--save_gif", action="store_true", help="save images AND a gif") + p.add_argument("--viewer", action="store_true", help="show the pygame viewer") + return p.parse_args() + + +def main(): + args = parse_args() + + run_dir = config.BASE_DIR / "experiments" / args.experiment / "run_0001" + # run_dir = next_run_dir(args.experiment) + + agent_pair = create_agent_pair(args.experiment, args.experiment) + + episode = run_game(agent_pair, args.layout, args.horizon) + episode_dir = run_dir / "episode_000001" + # episode_dir = next_episode_dir(run_dir) + + if args.save_gif: + frames_dir = episode_dir / "frames" + save_gif(episode.trajectories, frames_dir, fps=config.GIF_FPS) + elif args.save_frames: + save_frames(episode.trajectories, frames_dir) + + if args.viewer: + run_viewer(episode) + + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/Sybe/overcooked_irl/agent_factory.py b/Sybe/overcooked_irl/agent_factory.py new file mode 100644 index 00000000..7feeb7e9 --- /dev/null +++ b/Sybe/overcooked_irl/agent_factory.py @@ -0,0 +1,21 @@ +from overcooked_ai_py.agents.agent import AgentPair, RandomAgent + +def create_agent(agent_type): + if agent_type == "random": + return RandomAgent() + + elif agent_type == "ddqn": + raise NotImplementedError + + elif agent_type == "curiosity": + raise NotImplementedError + + else: + raise ValueError(f"Unknown agent type: {agent_type}") + + +def create_agent_pair(agent1_type, agent2_type): + agent1 = create_agent(agent1_type) + agent2 = create_agent(agent2_type) + + return AgentPair(agent1, agent2) \ No newline at end of file diff --git a/Sybe/overcooked_irl/check_env.py b/Sybe/overcooked_irl/check_env.py new file mode 100644 index 00000000..5016522a --- /dev/null +++ b/Sybe/overcooked_irl/check_env.py @@ -0,0 +1,8 @@ +import sys +import os + +print("--- Python Environment Check ---") +print(f"Python Version: {sys.version.split()[0]}") +print(f"Executable Path: {sys.executable}") +print(f"Working Directory: {os.getcwd()}") +print("----------------------------------") \ No newline at end of file diff --git a/Sybe/overcooked_irl/export.py b/Sybe/overcooked_irl/export.py new file mode 100644 index 00000000..1cceda06 --- /dev/null +++ b/Sybe/overcooked_irl/export.py @@ -0,0 +1,39 @@ +"""Turning states/trajectories into image files (PNG frames, GIF).""" +from pathlib import Path + +import imageio +from overcooked_ai_py.mdp.overcooked_env import OvercookedEnv +from overcooked_ai_py.mdp.overcooked_mdp import OvercookedGridworld +from overcooked_ai_py.visualization.state_visualizer import StateVisualizer + +def save_frames(trajectory, out_dir): + """Render a trajectory into individual PNG frames.""" + out_dir = Path(out_dir) + out_dir.mkdir(parents=True, exist_ok=True) + + StateVisualizer().display_rendered_trajectory( + trajectories=trajectory, + img_directory_path=str(out_dir), + ipython_display=False, + ) + + return out_dir + +def stitch_gif(frames_dir, gif_name="_trajectory.gif", fps=10): + """Combine the numbered PNGs in frames_dir into a GIF.""" + frames_dir = Path(frames_dir) + frame_files = sorted( + frames_dir.glob("*.png"), + key=lambda p: int("".join(c for c in p.stem if c.isdigit())), + ) + images = [imageio.imread(f) for f in frame_files] + gif_path = frames_dir / gif_name + imageio.mimsave(gif_path, images, duration=1 / fps, loop=0) + print(f"GIF saved to: {gif_path}") + return gif_path + + +def save_gif(trajectory, out_dir, fps=10): + """Render a trajectory and combine its frames into a GIF.""" + frames_dir = save_frames(trajectory, out_dir) + return stitch_gif(frames_dir, fps=fps) \ No newline at end of file diff --git a/Sybe/overcooked_irl/paths.py b/Sybe/overcooked_irl/paths.py new file mode 100644 index 00000000..c83634be --- /dev/null +++ b/Sybe/overcooked_irl/paths.py @@ -0,0 +1,18 @@ +import config + +def next_run_dir(experiment): + """Create and return experiments//run_XXXX""" + if experiment not in config.EXPERIMENTS: + raise ValueError(f"Unknown experiment: {experiment}. Must be one of {config.EXPERIMENTS}") + exp_dir = config.BASE_DIR / "experiments" / experiment + numbers = [int(p.name[4:]) for p in exp_dir.glob("run_*") if p.name[4:].isdigit()] + run_dir = exp_dir / f"run_{max(numbers, default=0) + 1:04d}" + run_dir.mkdir(parents=True, exist_ok=True) + return run_dir + +def next_episode_dir(run_dir): + """Create and return experiments//run_XXXX/episode_XXXXXX""" + numbers = [int(p.name[8:]) for p in run_dir.glob("episode_*") if p.name[8:].isdigit()] + episode_dir = run_dir / f"episode_{max(numbers, default=0) + 1:06d}" + episode_dir.mkdir(parents=True, exist_ok=True) + return episode_dir \ No newline at end of file diff --git a/Sybe/overcooked_irl/simulation.py b/Sybe/overcooked_irl/simulation.py new file mode 100644 index 00000000..d6fb800c --- /dev/null +++ b/Sybe/overcooked_irl/simulation.py @@ -0,0 +1,46 @@ +"""Running games and packaging the results. No rendering or UI in here.""" +from dataclasses import dataclass + +from overcooked_ai_py.agents.agent import AgentPair, RandomAgent +from overcooked_ai_py.agents.benchmarking import AgentEvaluator + + +@dataclass +class Episode: + """Data from one finished game""" + trajectories: dict # raw output of evaluate_agent_pair (needed by StateVisualizer) + states: list # OvercookedState per timestep + rewards: list # sparse reward per timestep + grid: list # terrain matrix + total_return: int + horizon: int + + @classmethod + def from_trajectories(cls, trajectories, evaluator, game_idx=0): + return cls( + trajectories=trajectories, + states=trajectories["ep_states"][game_idx], + rewards=trajectories["ep_rewards"][game_idx], + grid=evaluator.env.mdp.terrain_mtx, + total_return=int(trajectories["ep_returns"][game_idx]), + horizon=evaluator.env.horizon, + ) + + +def make_evaluator(layout_name, horizon): + return AgentEvaluator.from_layout_name( + mdp_params={"layout_name": layout_name}, + env_params={"horizon": horizon}, + ) + + +def random_agent_pair(): + return AgentPair(RandomAgent(), RandomAgent()) + + +def run_game(agent_pair, layout_name, horizon): + """Play one game with any AgentPair""" + print(f"Simulating '{layout_name}' for {horizon} steps...") + evaluator = make_evaluator(layout_name, horizon) + trajectories = evaluator.evaluate_agent_pair(agent_pair, num_games=1) + return Episode.from_trajectories(trajectories, evaluator) \ No newline at end of file diff --git a/Sybe/overcooked_irl/viewer.py b/Sybe/overcooked_irl/viewer.py new file mode 100644 index 00000000..6d30a574 --- /dev/null +++ b/Sybe/overcooked_irl/viewer.py @@ -0,0 +1,72 @@ +"""Interactive pygame trajectory viewer.""" +import pygame +from overcooked_ai_py.visualization.state_visualizer import StateVisualizer + +import config as config + + +def run_viewer(trajectory, fps=config.VIEWER_FPS): + pygame.init() + pygame.key.set_repeat(500, 30) # hold a key: 0.5s delay, then ~33 steps/s + + viz = StateVisualizer() + font = pygame.font.SysFont(None, 24) + n_steps = len(trajectory.states) + + def render(t): + return viz.render_state(trajectory.states[t], trajectory.grid) + + def draw_hud(screen, t): + current_score = int(sum(trajectory.rewards[:t])) if t > 0 else 0 + time_left = max(0, trajectory.horizon - t) + screen.blit(font.render(f"TIME LEFT: {time_left}", True, (255, 255, 255)), (20, 10)) + screen.blit( + font.render(f"SCORE: {current_score} / {trajectory.total_return}", True, (255, 255, 255)), + (20, 35), + ) + + width, height = render(0).get_size() + screen = pygame.display.set_mode((width, height + config.HUD_HEIGHT)) + clock = pygame.time.Clock() + + t, playing, running = 0, False, True + game_surface, rendered_t = render(0), 0 + + print("\n--- CONTROLS ---") + print("Right/Left : step forward/backward (hold to repeat)") + print("Space : play / pause") + print("Esc : quit\n") + + while running: + for event in pygame.event.get(): + if event.type == pygame.QUIT: + running = False + elif event.type == pygame.KEYDOWN: + if event.key == pygame.K_ESCAPE: + running = False + elif event.key == pygame.K_RIGHT: + t, playing = min(t + 1, n_steps - 1), False + elif event.key == pygame.K_LEFT: + t, playing = max(t - 1, 0), False + elif event.key == pygame.K_SPACE: + playing = not playing + + if playing: + if t < n_steps - 1: + t += 1 + else: + playing = False + + if t != rendered_t: # only re-render when the frame changes + game_surface, rendered_t = render(t), t + + screen.fill(config.BG_COLOR) + draw_hud(screen, t) + screen.blit(game_surface, (0, config.HUD_HEIGHT)) + pygame.display.set_caption( + f"Replay - Step {t}/{n_steps - 1} {'[PLAYING]' if playing else '[PAUSED]'}" + ) + pygame.display.flip() + clock.tick(fps) + + pygame.quit() \ No newline at end of file diff --git a/src/overcooked_demo/server/requirements.txt b/src/overcooked_demo/server/requirements.txt index a9810c76..cc736138 100644 --- a/src/overcooked_demo/server/requirements.txt +++ 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