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Functions176 in github.com/NeuralMMO/baselines

↓ 10 callersMethod__init__
(self, env, input_size=256, hidden_size=256, task_size=2048)
agent_zoo/yaofeng/policy.py:24
↓ 10 callersFunctionorthogonal_init
(layer, gain=1.0)
agent_zoo/yaofeng/policy.py:18
↓ 8 callersMethod__init__
(self, env, input_size=256, hidden_size=256, task_size=2048)
agent_zoo/neurips23_start_kit/baseline_policy.py:25
↓ 8 callersMethod__init__
(self, env, policy, input_size=256, hidden_size=256, num_layers=0)
agent_zoo/takeru/policy.py:17
↓ 7 callersMethodclose
(self)
curriculum_generation/task_encoder.py:167
↓ 5 callersFunctioncreate_basic_tasks
(unit_count)
syllabus_wrapper.py:58
↓ 5 callersFunctionget_init_args
(fn)
train.py:40
↓ 5 callersMethodget_task_embedding
Compute embeddings for given task specifications and save them to file. Args: task_spec_list: List of task specifications.
curriculum_generation/task_encoder.py:137
↓ 3 callersMethodseed
(self, seed)
reinforcement_learning/stat_wrapper.py:26
↓ 2 callersMethod_construct_prompt
Construct a task-specific prompt. Args: reward_to: Reward given to the agent upon successful completion of the task.
curriculum_generation/task_encoder.py:115
↓ 2 callersMethod_get_embedding
Compute the embeddings of tasks. Args: prompts: List of tasks defined as prompts. Returns: A list of embedd
curriculum_generation/task_encoder.py:66
↓ 2 callersMethod_get_task_deps_src
Extract source code and dependent functions of the evaluation function. Args: eval_fn: Function for task evaluation.
curriculum_generation/task_encoder.py:90
↓ 2 callersMethod_reset_episode_stats
(self)
reinforcement_learning/stat_wrapper.py:100
↓ 2 callersFunctioncombine_config_args
(parser, args, config)
train.py:85
↓ 2 callersFunctioncount_unique_events
(tick_log, experienced, every_event_to_count=EVERY_EVENT_TO_COUNT)
reinforcement_learning/stat_wrapper.py:295
↓ 2 callersFunctiondone_training
(data)
reinforcement_learning/clean_pufferl.py:574
↓ 2 callersFunctionevent_key_to_str
(event_key)
analysis/proc_task_cond_result.py:22
↓ 2 callersFunctionextract_module_fn
(module: ModuleType)
curriculum_generation/task_encoder.py:16
↓ 2 callersFunctiongenerate_replay
(args, env_creator, agent_creator, stop_when_all_complete_task=True, seed=None)
train_helper.py:92
↓ 2 callersFunctionget_eval_config
(debug=False)
evaluate.py:25
↓ 2 callersFunctionget_task_predicate
(spec)
neurips23_evaluation/export_embeddings.py:21
↓ 2 callersFunctioninit_wandb
(args, resume=True)
train_helper.py:21
↓ 2 callersFunctionload_from_config
(agent, debug=False)
train.py:20
↓ 2 callersFunctionmake_agent_creator
()
evaluate.py:97
↓ 2 callersFunctionmake_env_creator
(task_file, mode)
evaluate.py:80
↓ 2 callersMethodobservation
Called before observations are returned from the environment Use this to define custom featurizers. Changing the space itself requires you to
reinforcement_learning/stat_wrapper.py:29
↓ 2 callersMethodobservation
Called before observations are returned from the environment Use this to define custom featurizers. Changing the space itself requires you to
agent_zoo/takeru/reward_wrapper.py:25
↓ 2 callersFunctionprint_dashboard
(stats, init_performance, performance)
reinforcement_learning/clean_pufferl.py:642
↓ 2 callersMethodreset
(self, **kwargs)
syllabus_wrapper.py:129
↓ 2 callersFunctionsave_checkpoint
(data)
reinforcement_learning/clean_pufferl.py:578
↓ 2 callersFunctiontrain
(args, env_creator, agent_creator, syllabus=None)
train_helper.py:49
↓ 2 callersFunctionupdate_args
(args, mode=None)
train.py:111
↓ 1 callersMethod_process_stats_and_early_stop
Update stats + info and save replays.
reinforcement_learning/stat_wrapper.py:112
↓ 1 callersMethod_reset_reward_vars
(self)
agent_zoo/neurips23_start_kit/reward_wrapper.py:30
↓ 1 callersMethod_reset_reward_vars
(self)
agent_zoo/yaofeng/reward_wrapper.py:53
↓ 1 callersMethod_save_results
(self, results, file_name)
evaluate.py:193
↓ 1 callersMethodaction
Called before actions are passed from the model to the environment
reinforcement_learning/stat_wrapper.py:35
↓ 1 callersMethodapply_layer
(self, layer, embeddings, mask, hidden)
agent_zoo/neurips23_start_kit/baseline_policy.py:222
↓ 1 callersMethodapply_layer
(self, layer, embeddings, mask, hidden)
agent_zoo/yaofeng/policy.py:288
↓ 1 callersMethodapply_layer
(self, layer, embeddings, mask, hidden)
agent_zoo/takeru/policy.py:327
↓ 1 callersMethodchange_task
(self, new_task)
syllabus_wrapper.py:155
↓ 1 callersFunctioncreate_pool
(num_proc)
curriculum_generation/manual_curriculum.py:325
↓ 1 callersFunctioncreate_sequential_curriculum
(task_space)
syllabus_wrapper.py:36
↓ 1 callersFunctionextract_task_name
(task_str)
analysis/proc_task_cond_result.py:48
↓ 1 callersFunctiongather_agent_events_by_task
(data_dir)
analysis/proc_task_cond_result.py:54
↓ 1 callersFunctionget_event_stats
(task_name, task_data)
analysis/proc_task_cond_result.py:72
↓ 1 callersMethodget_learnable_tasks
( self, num_tasks, max_completed=0.8, # filter out easy tasks min_completed=0
curriculum_generation/task_sampler.py:46
↓ 1 callersMethodget_pufferl_config
(debug=False)
evaluate.py:151
↓ 1 callersFunctionget_summary_dict
(progress, key)
analysis/proc_eval_result.py:42
↓ 1 callersFunctionget_task_weight
(task_name)
analysis/proc_eval_result.py:34
↓ 1 callersMethodno_explore_post_processing
(self, logits)
agent_zoo/takeru/policy.py:66
↓ 1 callersMethodperform_eval
(self, mode, task_file, seed, num_eval_episode, save_file_prefix)
evaluate.py:164
↓ 1 callersFunctionprocess_eval_files
(policy_store_dir, eval_prefix)
analysis/proc_eval_result.py:79
↓ 1 callersFunctionprocess_event_log
Process the event log and extract performed actions and achievements.
reinforcement_learning/stat_wrapper.py:216
↓ 1 callersMethodreward_terminated_truncated_info
Called on reward, terminated, truncated, and info before they are returned from the environment Use this to define custom reward shaping.
reinforcement_learning/stat_wrapper.py:39
↓ 1 callersMethodrun
( self, mode, task_file=EVAL_TASK_FILE, seed=None, num_episode=None, save_file_prefix=None )
evaluate.py:197
↓ 1 callersFunctionseed_everything
(seed, torch_deterministic)
reinforcement_learning/clean_pufferl.py:613
↓ 1 callersMethodsequential_task_list
(self)
syllabus_wrapper.py:170
↓ 1 callersFunctionsetup_agent
(module_name)
train.py:59
↓ 1 callersMethodsetup_evaluator
(self, mode, task_file, seed)
evaluate.py:119
↓ 1 callersMethodstep
(self, action)
reinforcement_learning/stat_wrapper.py:57
↓ 1 callersFunctionsummarize_single_eval
(data, weighted_score=False)
analysis/proc_eval_result.py:54
↓ 1 callersFunctionsweep
(args, env_creator, agent_creator)
train_helper.py:72
↓ 1 callersFunctionunroll_nested_dict
(d)
reinforcement_learning/clean_pufferl.py:630
↓ 1 callersMethodupdate
(self, infos, prefix="curriculum/")
curriculum_generation/task_sampler.py:25
FunctionPracticeEating
The progress, the max of which is 1, should * increase small for each eating * increase big for the 1st and 3rd eating * reach 1 with 10 e
curriculum_generation/curriculum_tutorial.py:45
MethodPracticeFormation
(gs, subject, dist, num_tick)
syllabus_wrapper.py:303
FunctionPracticeInventoryManagement
(gs, subject, space, num_tick)
curriculum_generation/manual_curriculum.py:201
FunctionPracticeInventoryManagement
(gs, subject, space, num_tick)
curriculum_generation/curriculum_tutorial.py:64
MethodPracticeInventoryManagement
(gs, subject, space, num_tick)
syllabus_wrapper.py:395
FunctionPracticeSkillWithTool
(gs, subject, skill, exp)
curriculum_generation/manual_curriculum.py:119
Method__enter__
(self)
curriculum_generation/task_encoder.py:174
Method__exit__
(self, exc_type, exc_value, traceback)
curriculum_generation/task_encoder.py:177
Method__init__
(self, env: gym.Env)
syllabus_wrapper.py:83
Method__init__
(self, task_file, mode)
evaluate.py:49
Method__init__
(self, policy_store_dir, debug=False)
evaluate.py:112
Method__init__
(self, env_args: Namespace)
reinforcement_learning/environment.py:28
Method__init__
( self, env, eval_mode=False, early_stop_agent_num=0, stat_prefix=None
reinforcement_learning/stat_wrapper.py:10
Method__init__
(self, env, policy, input_size=256, hidden_size=256, num_layers=1)
agent_zoo/neurips23_start_kit/baseline_policy.py:18
Method__init__
(self, input_size)
agent_zoo/neurips23_start_kit/baseline_policy.py:86
Method__init__
(self, input_size, hidden_size)
agent_zoo/neurips23_start_kit/baseline_policy.py:116
Method__init__
(self, input_size, hidden_size)
agent_zoo/neurips23_start_kit/baseline_policy.py:149
Method__init__
(self, input_size, hidden_size)
agent_zoo/neurips23_start_kit/baseline_policy.py:174
Method__init__
(self, input_size, hidden_size)
agent_zoo/neurips23_start_kit/baseline_policy.py:185
Method__init__
(self, input_size, hidden_size, task_size)
agent_zoo/neurips23_start_kit/baseline_policy.py:194
Method__init__
(self, input_size, hidden_size)
agent_zoo/neurips23_start_kit/baseline_policy.py:203
Method__init__
( # BaseStatWrapper args self, env, eval_mode=False, early_stop_agent_
agent_zoo/neurips23_start_kit/reward_wrapper.py:5
Method__init__
(self, env, policy, input_size=256, hidden_size=256, num_layers=2)
agent_zoo/yaofeng/policy.py:14
Method__init__
(self, in_planes, img_size=(15, 15))
agent_zoo/yaofeng/policy.py:75
Method__init__
(self, input_size)
agent_zoo/yaofeng/policy.py:92
Method__init__
(self, input_size, hidden_size, output_size, num_layers=2)
agent_zoo/yaofeng/policy.py:119
Method__init__
(self, input_size, hidden_size)
agent_zoo/yaofeng/policy.py:139
Method__init__
(self, input_size, hidden_size)
agent_zoo/yaofeng/policy.py:188
Method__init__
(self, input_size, hidden_size)
agent_zoo/yaofeng/policy.py:233
Method__init__
(self, input_size, hidden_size)
agent_zoo/yaofeng/policy.py:246
Method__init__
(self, input_size, hidden_size, task_size)
agent_zoo/yaofeng/policy.py:257
Method__init__
(self, input_size, hidden_size)
agent_zoo/yaofeng/policy.py:268
Method__init__
( # BaseStatWrapper args self, env, eval_mode=False, early_stop_agent_
agent_zoo/yaofeng/reward_wrapper.py:12
Method__init__
(self, env, input_size=256, hidden_size=256, task_size=2048)
agent_zoo/takeru/policy.py:24
Method__init__
(self, input_size)
agent_zoo/takeru/policy.py:86
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