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| $ PYTHONPATH=. python3 -u main.py setup=metaworld agent=state_sac env=metaworld-mt10 agent.multitask.num_envs=10 agent.multitask.should_use_disentangled_alpha=True | |
| /home/tony/miniconda3/envs/mtrl/lib/python3.7/site-packages/gym/envs/registration.py:416: UserWarning: WARN: The `registry.env_specs` property along with `EnvSpecTree` is deprecated. Please use `registry` directly as a dictionary instead. | |
| "The `registry.env_specs` property along with `EnvSpecTree` is deprecated. Please use `registry` directly as a dictionary instead." | |
| setup: | |
| seed: 42 | |
| setup: metaworld | |
| base_path: /home/tony/lab/mtrl | |
| save_dir: ${setup.base_path}/logs/${setup.id} | |
| device: cuda:0 | |
| id: 90f2497ff4cee27c0d30fbc66e6ba205f94808ba4ea16e057df58e73_issue_None_seed_42 | |
| description: Sample Task | |
| tags: null | |
| git: | |
| commit_id: e282c3f5a205970b4ad7d1c1ebae7aa9b4d56218 | |
| has_uncommitted_changes: null | |
| issue_id: null | |
| date: '2022-06-28 16:30:39' | |
| slurm_id: '-1' | |
| debug: | |
| should_enable: false | |
| experiment: | |
| name: metaworld | |
| builder: | |
| _target_: mtrl.experiment.${experiment.name}.Experiment | |
| init_steps: 1500 | |
| num_train_steps: 1000000 | |
| eval_freq: 10000 | |
| num_eval_episodes: 10 | |
| should_resume: true | |
| save: | |
| model: | |
| retain_last_n: 1 | |
| buffer: | |
| should_save: true | |
| size_per_chunk: 10000 | |
| num_samples_to_save: -1 | |
| save_dir: ${setup.save_dir} | |
| save_video: false | |
| envs_to_exclude_during_training: null | |
| agent: | |
| name: state_sac | |
| encoder_feature_dim: 50 | |
| num_layers: 0 | |
| num_filters: 0 | |
| builder: | |
| _target_: mtrl.agent.sac.Agent | |
| actor_cfg: ${agent.actor} | |
| critic_cfg: ${agent.critic} | |
| multitask_cfg: ${agent.multitask} | |
| alpha_optimizer_cfg: ${agent.optimizers.alpha} | |
| actor_optimizer_cfg: ${agent.optimizers.actor} | |
| critic_optimizer_cfg: ${agent.optimizers.critic} | |
| discount: 0.99 | |
| init_temperature: 1.0 | |
| actor_update_freq: 1 | |
| critic_tau: 0.005 | |
| critic_target_update_freq: 1 | |
| encoder_tau: 0.05 | |
| actor: | |
| _target_: mtrl.agent.components.actor.Actor | |
| num_layers: 3 | |
| hidden_dim: 400 | |
| log_std_bounds: | |
| - -20 | |
| - 2 | |
| encoder_cfg: ${agent.encoder} | |
| multitask_cfg: ${agent.multitask} | |
| critic: | |
| _target_: mtrl.agent.components.critic.Critic | |
| hidden_dim: ${agent.actor.hidden_dim} | |
| num_layers: ${agent.actor.num_layers} | |
| encoder_cfg: ${agent.encoder} | |
| multitask_cfg: ${agent.multitask} | |
| encoder: | |
| type_to_select: identity | |
| identity: | |
| type: identity | |
| feature_dim: ${agent.encoder_feature_dim} | |
| feedforward: | |
| type: feedforward | |
| hidden_dim: 50 | |
| num_layers: 2 | |
| feature_dim: ${agent.encoder_feature_dim} | |
| should_tie_encoders: true | |
| film: | |
| type: film | |
| hidden_dim: 50 | |
| num_layers: 2 | |
| feature_dim: ${agent.encoder_feature_dim} | |
| should_tie_encoders: true | |
| moe: | |
| type: moe | |
| encoder_cfg: | |
| type: feedforward | |
| hidden_dim: 50 | |
| num_layers: 2 | |
| feature_dim: ${agent.encoder_feature_dim} | |
| should_tie_encoders: true | |
| num_experts: 9 | |
| task_id_to_encoder_id_cfg: | |
| mode: cluster | |
| num_envs: ${env.num_envs} | |
| gate: | |
| embedding_dim: 50 | |
| hidden_dim: 50 | |
| num_layers: 2 | |
| temperature: 1.0 | |
| should_use_soft_attention: false | |
| topk: 2 | |
| task_encoder_cfg: | |
| should_use_task_encoding: true | |
| should_detach_task_encoding: true | |
| attention: | |
| embedding_dim: 50 | |
| hidden_dim: 50 | |
| num_layers: 2 | |
| temperature: 1.0 | |
| should_use_soft_attention: true | |
| task_encoder_cfg: | |
| should_use_task_encoding: true | |
| should_detach_task-encoding: true | |
| cluster: | |
| env_name: ${env.name} | |
| task_description: ${env.description} | |
| ordered_task_list: ${env.ordered_task_list} | |
| mapping_cfg: ${agent.task_to_encoder_cluster} | |
| num_eval_episodes: ${experiment.num_eval_episodes} | |
| batch_size: ${replay_buffer.batch_size} | |
| identity: | |
| num_eval_episodes: ${experiment.num_eval_episodes} | |
| batch_size: ${replay_buffer.batch_size} | |
| ensemble: | |
| num_eval_episodes: ${experiment.num_eval_episodes} | |
| batch_size: ${replay_buffer.batch_size} | |
| factorized_moe: | |
| type: fmoe | |
| encoder_cfg: ${agent.encoder.feedforward} | |
| num_factors: 2 | |
| num_experts_per_factor: | |
| - 5 | |
| - 5 | |
| pixel: | |
| type: pixel | |
| feature_dim: ${agent.encoder_feature_dim} | |
| num_filters: ${agent.num_filters} | |
| num_layers: ${agent.num_layers} | |
| transition_model: | |
| _target_: mtrl.agent.components.transition_model.make_transition_model | |
| transition_cfg: | |
| type: '' | |
| feature_dim: ${agent.encoder_feature_dim} | |
| layer_width: 512 | |
| multitask_cfg: ${agent.multitask} | |
| mask: | |
| num_tasks: ${env.num_envs} | |
| num_eval_episodes: ${experiment.num_eval_episodes} | |
| batch_size: ${replay_buffer.batch_size} | |
| multitask: | |
| num_envs: 10 | |
| should_use_disentangled_alpha: true | |
| should_use_task_encoder: false | |
| should_use_multi_head_policy: false | |
| should_use_disjoint_policy: false | |
| task_encoder_cfg: | |
| model_cfg: | |
| _target_: mtrl.agent.components.task_encoder.TaskEncoder | |
| pretrained_embedding_cfg: | |
| should_use: false | |
| path_to_load_from: /private/home/sodhani/projects/mtrl/metadata/task_embedding/roberta_small/${env.name}.json | |
| ordered_task_list: ${env.ordered_task_list} | |
| num_embeddings: ${agent.multitask.num_envs} | |
| embedding_dim: 50 | |
| hidden_dim: 50 | |
| num_layers: 2 | |
| output_dim: 50 | |
| optimizer_cfg: ${agent.optimizers.actor} | |
| losses_to_train: | |
| - critic | |
| - transition_reward | |
| - decoder | |
| - task_encoder | |
| multi_head_policy_cfg: | |
| mask_cfg: ${agent.mask} | |
| actor_cfg: | |
| should_condition_model_on_task_info: false | |
| should_condition_encoder_on_task_info: true | |
| should_concatenate_task_info_with_encoder: true | |
| moe_cfg: | |
| mode: soft_modularization | |
| num_experts: 4 | |
| should_use: false | |
| critic_cfg: ${agent.multitask.actor_cfg} | |
| gradnorm: | |
| alpha: 1.0 | |
| task_to_encoder_cluster: | |
| mt10: | |
| cluster: | |
| action_close: | |
| - close | |
| action_default: | |
| - insert | |
| - pick and place | |
| - press | |
| - reach | |
| action_open: | |
| - open | |
| action_push: | |
| - push | |
| object_default: | |
| - button | |
| - door | |
| - peg | |
| - revolving joint | |
| object_drawer: | |
| - drawer | |
| object_goal: | |
| - goal | |
| object_puck: | |
| - puck | |
| object_window: | |
| - window | |
| mt50: | |
| cluster: | |
| action_close: | |
| - close | |
| action_default: | |
| - insert | |
| - pick and place | |
| - press | |
| - reach | |
| action_open: | |
| - open | |
| action_push: | |
| - push | |
| object_default: | |
| - button | |
| - door | |
| - peg | |
| - revolving joint | |
| object_drawer: | |
| - drawer | |
| object_goal: | |
| - goal | |
| object_puck: | |
| - puck | |
| object_window: | |
| - window | |
| optimizers: | |
| actor: | |
| _target_: torch.optim.Adam | |
| lr: 0.0003 | |
| betas: | |
| - 0.9 | |
| - 0.999 | |
| alpha: | |
| _target_: torch.optim.Adam | |
| lr: 0.0003 | |
| betas: | |
| - 0.9 | |
| - 0.999 | |
| critic: | |
| _target_: torch.optim.Adam | |
| lr: 0.0003 | |
| betas: | |
| - 0.9 | |
| - 0.999 | |
| decoder: | |
| _target_: torch.optim.Adam | |
| lr: 0.0003 | |
| betas: | |
| - 0.9 | |
| - 0.999 | |
| weight_decay: 1.0e-07 | |
| encoder: | |
| _target_: torch.optim.Adam | |
| lr: 0.0003 | |
| betas: | |
| - 0.9 | |
| - 0.999 | |
| env: | |
| name: metaworld-mt10 | |
| num_envs: 10 | |
| benchmark: | |
| _target_: metaworld.MT10 | |
| builder: | |
| make_kwargs: | |
| should_perform_reward_normalization: true | |
| dummy: | |
| _target_: metaworld.MT1 | |
| env_name: pick-place-v1 | |
| description: | |
| reach-v1: Reach a goal position. Randomize the goal positions. | |
| push-v1: Push the puck to a goal. Randomize puck and goal positions. | |
| pick-place-v1: Pick and place a puck to a goal. Randomize puck and goal positions. | |
| door-open-v1: Open a door with a revolving joint. Randomize door positions. | |
| drawer-open-v1: Open a drawer. Randomize drawer positions. | |
| drawer-close-v1: Push and close a drawer. Randomize the drawer positions. | |
| button-press-topdown-v1: Press a button from the top. Randomize button positions. | |
| peg-insert-side-v1: Insert a peg sideways. Randomize peg and goal positions. | |
| window-open-v1: Push and open a window. Randomize window positions. | |
| window-close-v1: Push and close a window. Randomize window positions. | |
| ordered_task_list: null | |
| replay_buffer: | |
| _target_: mtrl.replay_buffer.ReplayBuffer | |
| env_obs_shape: null | |
| action_shape: null | |
| capacity: 1000000 | |
| batch_size: 128 | |
| logger: | |
| _target_: mtrl.logger.Logger | |
| logger_dir: ${setup.save_dir} | |
| use_tb: false | |
| metrics: | |
| train: | |
| - - episode | |
| - E | |
| - int | |
| - average | |
| - - step | |
| - S | |
| - int | |
| - average | |
| - - duration | |
| - D | |
| - time | |
| - average | |
| - - episode_reward | |
| - R | |
| - float | |
| - average | |
| - - success | |
| - Su | |
| - float | |
| - average | |
| - - batch_reward | |
| - BR | |
| - float | |
| - average | |
| - - actor_loss | |
| - ALOSS | |
| - float | |
| - average | |
| - - critic_loss | |
| - CLOSS | |
| - float | |
| - average | |
| - - ae_loss | |
| - RLOSS | |
| - float | |
| - average | |
| - - ae_transition_loss | |
| - null | |
| - float | |
| - average | |
| - - reward_loss | |
| - null | |
| - float | |
| - average | |
| - - actor_target_entropy | |
| - null | |
| - float | |
| - average | |
| - - actor_entropy | |
| - null | |
| - float | |
| - average | |
| - - alpha_loss | |
| - null | |
| - float | |
| - average | |
| - - alpha_value | |
| - null | |
| - float | |
| - average | |
| - - contrastive_loss | |
| - MLOSS | |
| - float | |
| - average | |
| - - max_rat | |
| - MR | |
| - float | |
| - average | |
| - - env_index | |
| - ENV | |
| - str | |
| - constant | |
| - - episode_reward_env_index_ | |
| - R_ | |
| - float | |
| - average | |
| - - success_env_index_ | |
| - Su_ | |
| - float | |
| - average | |
| - - env_index_ | |
| - ENV_ | |
| - str | |
| - constant | |
| - - batch_reward_agent_index_ | |
| - null | |
| - float | |
| - average | |
| - - critic_loss_agent_index_ | |
| - AGENT_ | |
| - float | |
| - average | |
| - - actor_distilled_agent_loss_agent_index_ | |
| - null | |
| - float | |
| - average | |
| - - actor_loss_agent_index_ | |
| - null | |
| - float | |
| - average | |
| - - actor_target_entropy_agent_index_ | |
| - null | |
| - float | |
| - average | |
| - - actor_entropy_agent_index_ | |
| - null | |
| - float | |
| - average | |
| - - alpha_loss_agent_index_ | |
| - null | |
| - float | |
| - average | |
| - - alpha_value_agent_index_ | |
| - null | |
| - float | |
| - average | |
| - - ae_loss_agent_index_ | |
| - null | |
| - float | |
| - average | |
| eval: | |
| - - episode | |
| - E | |
| - int | |
| - average | |
| - - step | |
| - S | |
| - int | |
| - average | |
| - - episode_reward | |
| - R | |
| - float | |
| - average | |
| - - env_index | |
| - ENV | |
| - str | |
| - constant | |
| - - success | |
| - Su | |
| - float | |
| - average | |
| - - episode_reward_env_index_ | |
| - R_ | |
| - float | |
| - average | |
| - - success_env_index_ | |
| - Su_ | |
| - float | |
| - average | |
| - - env_index_ | |
| - ENV_ | |
| - str | |
| - constant | |
| - - batch_reward_agent_index_ | |
| - AGENT_ | |
| - float | |
| - average | |
| logbook: | |
| _target_: ml_logger.logbook.make_config | |
| write_to_console: false | |
| logger_dir: ${setup.save_dir} | |
| create_multiple_log_files: false | |
| [2022-06-28 16:30:40,084][default_logger][INFO] - {"setup": {"seed": 42, "setup": "metaworld", "base_path": "/home/tony/lab/mtrl", "save_dir": "${setup.base_path}/logs/${setup.id}", "device": "cuda:0", "id": "90f2497ff4cee27c0d30fbc66e6ba205f94808ba4ea16e057df58e73_issue_None_seed_42", "description": "Sample Task", "tags": null, "git": {"commit_id": "e282c3f5a205970b4ad7d1c1ebae7aa9b4d56218", "has_uncommitted_changes": null, "issue_id": null}, "date": "2022-06-28 16:30:39", "slurm_id": "-1", "debug": {"should_enable": false}}, "experiment": {"name": "metaworld", "builder": {"_target_": "mtrl.experiment.${experiment.name}.Experiment"}, "init_steps": 1500, "num_train_steps": 1000000, "eval_freq": 10000, "num_eval_episodes": 10, "should_resume": true, "save": {"model": {"retain_last_n": 1}, "buffer": {"should_save": true, "size_per_chunk": 10000, "num_samples_to_save": -1}}, "save_dir": "${setup.save_dir}", "save_video": false, "envs_to_exclude_during_training": null}, "agent": {"name": "state_sac", "encoder_feature_dim": 50, "num_layers": 0, "num_filters": 0, "builder": {"_target_": "mtrl.agent.sac.Agent", "actor_cfg": "${agent.actor}", "critic_cfg": "${agent.critic}", "multitask_cfg": "${agent.multitask}", "alpha_optimizer_cfg": "${agent.optimizers.alpha}", "actor_optimizer_cfg": "${agent.optimizers.actor}", "critic_optimizer_cfg": "${agent.optimizers.critic}", "discount": 0.99, "init_temperature": 1.0, "actor_update_freq": 1, "critic_tau": 0.005, "critic_target_update_freq": 1, "encoder_tau": 0.05}, "actor": {"_target_": "mtrl.agent.components.actor.Actor", "num_layers": 3, "hidden_dim": 400, "log_std_bounds": [-20, 2], "encoder_cfg": "${agent.encoder}", "multitask_cfg": "${agent.multitask}"}, "critic": {"_target_": "mtrl.agent.components.critic.Critic", "hidden_dim": "${agent.actor.hidden_dim}", "num_layers": "${agent.actor.num_layers}", "encoder_cfg": "${agent.encoder}", "multitask_cfg": "${agent.multitask}"}, "encoder": {"type_to_select": "identity", "identity": {"type": "identity", "feature_dim": "${agent.encoder_feature_dim}"}, "feedforward": {"type": "feedforward", "hidden_dim": 50, "num_layers": 2, "feature_dim": "${agent.encoder_feature_dim}", "should_tie_encoders": true}, "film": {"type": "film", "hidden_dim": 50, "num_layers": 2, "feature_dim": "${agent.encoder_feature_dim}", "should_tie_encoders": true}, "moe": {"type": "moe", "encoder_cfg": {"type": "feedforward", "hidden_dim": 50, "num_layers": 2, "feature_dim": "${agent.encoder_feature_dim}", "should_tie_encoders": true}, "num_experts": 9, "task_id_to_encoder_id_cfg": {"mode": "cluster", "num_envs": "${env.num_envs}", "gate": {"embedding_dim": 50, "hidden_dim": 50, "num_layers": 2, "temperature": 1.0, "should_use_soft_attention": false, "topk": 2, "task_encoder_cfg": {"should_use_task_encoding": true, "should_detach_task_encoding": true}}, "attention": {"embedding_dim": 50, "hidden_dim": 50, "num_layers": 2, "temperature": 1.0, "should_use_soft_attention": true, "task_encoder_cfg": {"should_use_task_encoding": true, "should_detach_task-encoding": true}}, "cluster": {"env_name": "${env.name}", "task_description": "${env.description}", "ordered_task_list": "${env.ordered_task_list}", "mapping_cfg": "${agent.task_to_encoder_cluster}", "num_eval_episodes": "${experiment.num_eval_episodes}", "batch_size": "${replay_buffer.batch_size}"}, "identity": {"num_eval_episodes": "${experiment.num_eval_episodes}", "batch_size": "${replay_buffer.batch_size}"}, "ensemble": {"num_eval_episodes": "${experiment.num_eval_episodes}", "batch_size": "${replay_buffer.batch_size}"}}}, "factorized_moe": {"type": "fmoe", "encoder_cfg": "${agent.encoder.feedforward}", "num_factors": 2, "num_experts_per_factor": [5, 5]}, "pixel": {"type": "pixel", "feature_dim": "${agent.encoder_feature_dim}", "num_filters": "${agent.num_filters}", "num_layers": "${agent.num_layers}"}}, "transition_model": {"_target_": "mtrl.agent.components.transition_model.make_transition_model", "transition_cfg": {"type": "", "feature_dim": "${agent.encoder_feature_dim}", "layer_width": 512}, "multitask_cfg": "${agent.multitask}"}, "mask": {"num_tasks": "${env.num_envs}", "num_eval_episodes": "${experiment.num_eval_episodes}", "batch_size": "${replay_buffer.batch_size}"}, "multitask": {"num_envs": 10, "should_use_disentangled_alpha": true, "should_use_task_encoder": false, "should_use_multi_head_policy": false, "should_use_disjoint_policy": false, "task_encoder_cfg": {"model_cfg": {"_target_": "mtrl.agent.components.task_encoder.TaskEncoder", "pretrained_embedding_cfg": {"should_use": false, "path_to_load_from": "/private/home/sodhani/projects/mtrl/metadata/task_embedding/roberta_small/${env.name}.json", "ordered_task_list": "${env.ordered_task_list}"}, "num_embeddings": "${agent.multitask.num_envs}", "embedding_dim": 50, "hidden_dim": 50, "num_layers": 2, "output_dim": 50}, "optimizer_cfg": "${agent.optimizers.actor}", "losses_to_train": ["critic", "transition_reward", "decoder", "task_encoder"]}, "multi_head_policy_cfg": {"mask_cfg": "${agent.mask}"}, "actor_cfg": {"should_condition_model_on_task_info": false, "should_condition_encoder_on_task_info": true, "should_concatenate_task_info_with_encoder": true, "moe_cfg": {"mode": "soft_modularization", "num_experts": 4, "should_use": false}}, "critic_cfg": "${agent.multitask.actor_cfg}"}, "gradnorm": {"alpha": 1.0}, "task_to_encoder_cluster": {"mt10": {"cluster": {"action_close": ["close"], "action_default": ["insert", "pick and place", "press", "reach"], "action_open": ["open"], "action_push": ["push"], "object_default": ["button", "door", "peg", "revolving joint"], "object_drawer": ["drawer"], "object_goal": ["goal"], "object_puck": ["puck"], "object_window": ["window"]}}, "mt50": {"cluster": {"action_close": ["close"], "action_default": ["insert", "pick and place", "press", "reach"], "action_open": ["open"], "action_push": ["push"], "object_default": ["button", "door", "peg", "revolving joint"], "object_drawer": ["drawer"], "object_goal": ["goal"], "object_puck": ["puck"], "object_window": ["window"]}}}, "optimizers": {"actor": {"_target_": "torch.optim.Adam", "lr": 0.0003, "betas": [0.9, 0.999]}, "alpha": {"_target_": "torch.optim.Adam", "lr": 0.0003, "betas": [0.9, 0.999]}, "critic": {"_target_": "torch.optim.Adam", "lr": 0.0003, "betas": [0.9, 0.999]}, "decoder": {"_target_": "torch.optim.Adam", "lr": 0.0003, "betas": [0.9, 0.999], "weight_decay": 1e-07}, "encoder": {"_target_": "torch.optim.Adam", "lr": 0.0003, "betas": [0.9, 0.999]}}}, "env": {"name": "metaworld-mt10", "num_envs": 10, "benchmark": {"_target_": "metaworld.MT10"}, "builder": {"make_kwargs": {"should_perform_reward_normalization": true}}, "dummy": {"_target_": "metaworld.MT1", "env_name": "pick-place-v1"}, "description": {"reach-v1": "Reach a goal position. Randomize the goal positions.", "push-v1": "Push the puck to a goal. Randomize puck and goal positions.", "pick-place-v1": "Pick and place a puck to a goal. Randomize puck and goal positions.", "door-open-v1": "Open a door with a revolving joint. Randomize door positions.", "drawer-open-v1": "Open a drawer. Randomize drawer positions.", "drawer-close-v1": "Push and close a drawer. Randomize the drawer positions.", "button-press-topdown-v1": "Press a button from the top. Randomize button positions.", "peg-insert-side-v1": "Insert a peg sideways. Randomize peg and goal positions.", "window-open-v1": "Push and open a window. Randomize window positions.", "window-close-v1": "Push and close a window. Randomize window positions."}, "ordered_task_list": null}, "replay_buffer": {"_target_": "mtrl.replay_buffer.ReplayBuffer", "env_obs_shape": null, "action_shape": null, "capacity": 1000000, "batch_size": 128}, "logger": {"_target_": "mtrl.logger.Logger", "logger_dir": "${setup.save_dir}", "use_tb": false}, "metrics": {"train": [["episode", "E", "int", "average"], ["step", "S", "int", "average"], ["duration", "D", "time", "average"], ["episode_reward", "R", "float", "average"], ["success", "Su", "float", "average"], ["batch_reward", "BR", "float", "average"], ["actor_loss", "ALOSS", "float", "average"], ["critic_loss", "CLOSS", "float", "average"], ["ae_loss", "RLOSS", "float", "average"], ["ae_transition_loss", null, "float", "average"], ["reward_loss", null, "float", "average"], ["actor_target_entropy", null, "float", "average"], ["actor_entropy", null, "float", "average"], ["alpha_loss", null, "float", "average"], ["alpha_value", null, "float", "average"], ["contrastive_loss", "MLOSS", "float", "average"], ["max_rat", "MR", "float", "average"], ["env_index", "ENV", "str", "constant"], ["episode_reward_env_index_", "R_", "float", "average"], ["success_env_index_", "Su_", "float", "average"], ["env_index_", "ENV_", "str", "constant"], ["batch_reward_agent_index_", null, "float", "average"], ["critic_loss_agent_index_", "AGENT_", "float", "average"], ["actor_distilled_agent_loss_agent_index_", null, "float", "average"], ["actor_loss_agent_index_", null, "float", "average"], ["actor_target_entropy_agent_index_", null, "float", "average"], ["actor_entropy_agent_index_", null, "float", "average"], ["alpha_loss_agent_index_", null, "float", "average"], ["alpha_value_agent_index_", null, "float", "average"], ["ae_loss_agent_index_", null, "float", "average"]], "eval": [["episode", "E", "int", "average"], ["step", "S", "int", "average"], ["episode_reward", "R", "float", "average"], ["env_index", "ENV", "str", "constant"], ["success", "Su", "float", "average"], ["episode_reward_env_index_", "R_", "float", "average"], ["success_env_index_", "Su_", "float", "average"], ["env_index_", "ENV_", "str", "constant"], ["batch_reward_agent_index_", "AGENT_", "float", "average"]]}, "logbook": {"_target_": "ml_logger.logbook.make_config", "write_to_console": false, "logger_dir": "${setup.save_dir}", "create_multiple_log_files": false}, "status": "RUNNING", "logbook_id": "0", "logbook_timestamp": "04:30:40PM CST Jun 28, 2022", "logbook_type": "metadata"} | |
| Starting Experiment at Tue Jun 28 16:30:40 2022 | |
| torch version = 1.7.1+cu110 | |
| /home/tony/miniconda3/envs/mtrl/lib/python3.7/site-packages/gym/envs/registration.py:416: UserWarning: WARN: The `registry.env_specs` property along with `EnvSpecTree` is deprecated. Please use `registry` directly as a dictionary instead. | |
| "The `registry.env_specs` property along with `EnvSpecTree` is deprecated. Please use `registry` directly as a dictionary instead." | |
| /home/tony/miniconda3/envs/mtrl/lib/python3.7/site-packages/gym/spaces/box.py:112: UserWarning: WARN: Box bound precision lowered by casting to float32 | |
| logger.warn(f"Box bound precision lowered by casting to {self.dtype}") | |
| Traceback (most recent call last): | |
| File "/home/tony/miniconda3/envs/mtrl/lib/python3.7/site-packages/hydra/utils.py", line 63, in call | |
| return _instantiate_class(type_or_callable, config, *args, **kwargs) | |
| File "/home/tony/miniconda3/envs/mtrl/lib/python3.7/site-packages/hydra/_internal/utils.py", line 500, in _instantiate_class | |
| return clazz(*args, **final_kwargs) | |
| File "/home/tony/lab/mtrl/mtrl/experiment/metaworld.py", line 20, in __init__ | |
| super().__init__(config, experiment_id) | |
| File "/home/tony/lab/mtrl/mtrl/experiment/multitask.py", line 25, in __init__ | |
| super().__init__(config, experiment_id) | |
| File "/home/tony/lab/mtrl/mtrl/experiment/experiment.py", line 33, in __init__ | |
| self.envs, self.env_metadata = self.build_envs() | |
| File "/home/tony/lab/mtrl/mtrl/experiment/metaworld.py", line 48, in build_envs | |
| config=self.config, benchmark=benchmark, mode=mode, env_id_to_task_map=None | |
| File "/home/tony/lab/mtrl/mtrl/env/builder.py", line 51, in build_metaworld_vec_env | |
| from mtenv.envs.metaworld.env import ( | |
| File "/home/tony/lab/mtrl/src/mtenv/mtenv/envs/__init__.py", line 15, in <module> | |
| "invalid_env_kwargs": [], | |
| File "/home/tony/lab/mtrl/src/mtenv/mtenv/envs/registration.py", line 74, in register | |
| return mtenv_registry.register(id, **kwargs) | |
| File "/home/tony/lab/mtrl/src/mtenv/mtenv/envs/registration.py", line 66, in register | |
| self.env_specs[id] = MultitaskEnvSpec(id, **kwargs) | |
| File "/home/tony/lab/mtrl/src/mtenv/mtenv/envs/registration.py", line 47, in __init__ | |
| kwargs=kwargs, | |
| File "<string>", line 10, in __init__ | |
| AttributeError: can't set attribute | |
| The above exception was the direct cause of the following exception: | |
| Traceback (most recent call last): | |
| File "/home/tony/miniconda3/envs/mtrl/lib/python3.7/site-packages/hydra/_internal/utils.py", line 198, in run_and_report | |
| return func() | |
| File "/home/tony/miniconda3/envs/mtrl/lib/python3.7/site-packages/hydra/_internal/utils.py", line 350, in <lambda> | |
| overrides=args.overrides, | |
| File "/home/tony/miniconda3/envs/mtrl/lib/python3.7/site-packages/hydra/_internal/hydra.py", line 112, in run | |
| configure_logging=with_log_configuration, | |
| File "/home/tony/miniconda3/envs/mtrl/lib/python3.7/site-packages/hydra/core/utils.py", line 128, in run_job | |
| ret.return_value = task_function(task_cfg) | |
| File "main.py", line 15, in launch | |
| return run(config) | |
| File "/home/tony/lab/mtrl/mtrl/app/run.py", line 35, in run | |
| experiment_utils.prepare_and_run(config=config) | |
| File "/home/tony/lab/mtrl/mtrl/experiment/utils.py", line 24, in prepare_and_run | |
| config.experiment.builder, config | |
| File "/home/tony/miniconda3/envs/mtrl/lib/python3.7/site-packages/hydra/utils.py", line 70, in call | |
| raise HydraException(f"Error calling '{cls}' : {e}") from e | |
| hydra.errors.HydraException: Error calling 'mtrl.experiment.metaworld.Experiment' : can't set attribute | |
| During handling of the above exception, another exception occurred: | |
| Traceback (most recent call last): | |
| File "main.py", line 19, in <module> | |
| launch() | |
| File "/home/tony/miniconda3/envs/mtrl/lib/python3.7/site-packages/hydra/main.py", line 37, in decorated_main | |
| strict=strict, | |
| File "/home/tony/miniconda3/envs/mtrl/lib/python3.7/site-packages/hydra/_internal/utils.py", line 347, in _run_hydra | |
| lambda: hydra.run( | |
| File "/home/tony/miniconda3/envs/mtrl/lib/python3.7/site-packages/hydra/_internal/utils.py", line 237, in run_and_report | |
| assert mdl is not None | |
| AssertionError |
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