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Functions2,539 in github.com/Yonv1943/Python

⨍ Functions 2,539◇ Types & classes 504
↓ 2 callersMethodadd_noise
(a, noise_std)
ElegantRL/elegantrl2/net.py:336
↓ 2 callersMethodadd_noise
(a, noise_std)
ElegantRL/AgentZoo/ElegantRL-MultiGPU/net.py:313
↓ 2 callersFunctionavg_update_net
(dst_net, src_net, device)
ElegantRL/Beta/elegantrl2/run.py:643
↓ 2 callersFunctionavg_update_optim
(dst_optim, src_optim, device)
ElegantRL/Beta/elegantrl2/run.py:636
↓ 2 callersFunctionavg_update_optim
(dst_optim, src_optim, device)
ElegantRL/elegantrl2/run.py:862
↓ 2 callersFunctionbuild_mlp
build MLP (MultiLayer Perceptron) dims: the middle dimension, `dims[-1]` is the output dimension of this network activation: the act
Demo_deep_learning/DEMO_DataParallel.py:40
↓ 2 callersFunctionbuild_vec_env
(env_class, env_args: dict, gpu_id: int = -1)
Demo_deep_learning/tutorial__subp_vec_env_gym.py:18
↓ 2 callersMethodcomm
(self, data, round_id)
ElegantRL/Beta/elegantrl2/run.py:620
↓ 2 callersMethodcompute__log_prob
(self, state, action)
ElegantRL/Beta/elegantrl/AgentZoo/ElegantRL-PER/net.py:151
↓ 2 callersMethodcompute_logprob
(self, state, action)
ElegantRL/Beta/elegantrl/tutorial/net.py:76
↓ 2 callersMethodcompute_logprob
(self, state, action)
ElegantRL/Beta/elegantrl/2021-04-04 ElegantRL/net.py:161
↓ 2 callersMethodcompute_logprob
(self, state, action)
ElegantRL/AgentZoo/ElegantRL-MultiGPU/net.py:150
↓ 2 callersMethodconfirm
(self, img)
Demo/DEMO_edge_detection.py:45
↓ 2 callersFunctionconvert_mp_to_np
(mp_array)
Demo/DEMO_mp_Array_Pipe.py:14
↓ 2 callersMethodcritic
(self, s, a)
ElegantRL/Beta/elegantrl/2021-04-04 ElegantRL/net.py:360
↓ 2 callersMethodcritic
(self, s, a)
ElegantRL/Beta/elegantrl/AgentZoo/ElegantRL-PER/net.py:343
↓ 2 callersMethodcritic
(self, s, a)
ElegantRL/elegantrl2/net.py:350
↓ 2 callersMethodcritic
(self, s, a)
ElegantRL/AgentZoo/ElegantRL-MultiGPU/net.py:327
↓ 2 callersFunctiondeepcopy_or_rebuild_env
(env)
ElegantRL/env.py:158
↓ 2 callersFunctiondeepcopy_or_rebuild_env
(env)
ElegantRL/Beta/env.py:179
↓ 2 callersFunctiondeepcopy_or_rebuild_env
(env)
ElegantRL/Beta/elegantrl/env.py:160
↓ 2 callersFunctiondeepcopy_or_rebuild_env
(env)
ElegantRL/Beta/elegantrl2/env.py:109
↓ 2 callersFunctiondeepcopy_or_rebuild_env
(env)
ElegantRL/elegantrl2/env.py:109
↓ 2 callersFunctiondraw_line__polar_coord
thickness=0 means don't draw
Demo/DEMO_cv2_MouseDetect.py:40
↓ 2 callersMethoddraw_plot
(self)
ElegantRL/Beta/elegantrl/2021-04-04 ElegantRL/run.py:492
↓ 2 callersMethodempty_buffer
we empty the buffer by set now_len=0. On-policy need to empty buffer before exploration
ElegantRL/replay.py:139
↓ 2 callersMethodempty_memories__before_explore
(self)
ElegantRL/Beta/elegantrl/AgentZoo/ElegantRL-PER/agent.py:1086
↓ 2 callersMethodevaluate
(self, model, batch_size, train_epoch, epoch, loss_avg)
Demo_deep_learning/classify_train_mp_ring.py:453
↓ 2 callersMethodevaluate
(self, model, batch_size, train_epoch, epoch, loss_avg)
Demo_deep_learning/classify_train_mp.py:427
↓ 2 callersMethodevaluate
(self, model, batch_size, train_epoch, epoch, loss_avg)
ElegantRL/Beta/DeepLearning/classify_train_mp_ring.py:453
↓ 2 callersMethodevaluate
(self, model, batch_size, train_epoch, epoch, loss_avg)
ElegantRL/Beta/DeepLearning/classify_train_mp.py:427
↓ 2 callersMethodevaluate
(self, model, batch_size, train_epoch, epoch, loss_avg)
ElegantRL/AgentZoo/ElegantRL-MultiGPU/classify_train_mp_ring.py:453
↓ 2 callersMethodevaluate
(self, model, batch_size, train_epoch, epoch, loss_avg)
ElegantRL/AgentZoo/ElegantRL-MultiGPU/classify_train_mp.py:427
↓ 2 callersMethodevaluate_act__save_checkpoint
(self, act, steps, obj_a, obj_c)
ElegantRL/Beta/elegantrl/AgentZoo/ElegantRL-PER/run.py:888
↓ 2 callersMethodevaluate_and_save
(self, act, steps, r_exp, log_tuple)
ElegantRL/elegantrl2/evaluator.py:39
↓ 2 callersMethodevaluate_and_save1
(self, agent_act, steps, r_exp, logging_tuple, if_train)
ElegantRL/elegantrl2/run.py:227
↓ 2 callersMethodevaluate_save
(self, act, steps, log_tuple)
ElegantRL/Beta/run.py:1002
↓ 2 callersMethodevaluate_save
(self, act, steps, log_tuple)
ElegantRL/Beta/elegantrl/run.py:1000
↓ 2 callersMethodevaluate_save
(self, act, steps, obj_a, obj_c)
ElegantRL/Beta/elegantrl/2021-04-04 ElegantRL/run.py:454
↓ 2 callersMethodevaluate_save
(self, act, steps, log_tuple)
ElegantRL/Beta/elegantrl2/run.py:673
↓ 2 callersMethodevaluate_save
(self, act, steps, obj_a, obj_c)
ElegantRL/AgentZoo/ElegantRL-MultiGPU/run.py:643
↓ 2 callersFunctionexplore_before_training
(env, target_step, reward_scale, gamma)
ElegantRL/run.py:1146
↓ 2 callersFunctionexplore_before_training
(env, buffer, target_step, reward_scale, gamma)
ElegantRL/Beta/elegantrl/2021-04-04 ElegantRL/run.py:583
↓ 2 callersFunctionexplore_before_training
(env, target_step, reward_scale, gamma)
ElegantRL/elegantrl2/run.py:819
↓ 2 callersFunctionexplore_before_training
(env, buffer, target_step, reward_scale, gamma)
ElegantRL/AgentZoo/ElegantRL-MultiGPU/run.py:759
↓ 2 callersMethodexplore_env
(self, env, buffer, target_step, reward_scale, gamma)
ElegantRL/Beta/elegantrl/2021-04-04 ElegantRL/agent.py:153
↓ 2 callersMethodexplore_env
(self, env, buffer, target_step, reward_scale, gamma)
ElegantRL/AgentZoo/ElegantRL-MultiGPU/agent.py:184
↓ 2 callersMethodexplore_envs
(self, env, target_step, reward_scale, gamma)
ElegantRL/agent.py:552
↓ 2 callersMethodextend_buffer
(self, state, other)
ElegantRL/replay.py:57
↓ 2 callersMethodextend_buffer
(self, state, other)
ElegantRL/Beta/replay.py:48
↓ 2 callersMethodextend_buffer
(self, state, other)
ElegantRL/Beta/elegantrl/replay.py:48
↓ 2 callersMethodextend_buffer
(self, state, other, i)
ElegantRL/Beta/elegantrl/2021-04-04 ElegantRL/replay.py:450
↓ 2 callersMethodextend_buffer
(self, state, other)
ElegantRL/Beta/elegantrl2/replay.py:48
↓ 2 callersMethodextend_buffer
(self, state, other, i)
ElegantRL/AgentZoo/ElegantRL-MultiGPU/agent.py:1174
↓ 2 callersMethodextend_buffer_from_list
(self, trajectory_list)
ElegantRL/Beta/replay.py:68
↓ 2 callersMethodextend_buffer_from_list
(self, trajectory_list)
ElegantRL/Beta/elegantrl/replay.py:68
↓ 2 callersMethodextend_buffer_from_list
(self, trajectory_list)
ElegantRL/Beta/elegantrl2/replay.py:68
↓ 2 callersMethodextend_buffer_from_list
(self, trajectory_list)
ElegantRL/Beta/elegantrl2/tutorial/agent.py:409
↓ 2 callersMethodextend_buffer_from_list
(self, trajectory_list)
ElegantRL/tutorial/agent.py:409
↓ 2 callersMethodfetch_data
(self)
ElegantRL/Beta/StockTrading.py:536
↓ 2 callersMethodfetch_data
Fetches data from Yahoo API Parameters ---------- Returns ------- `pd.DataFrame` 7 columns:
ElegantRL/Beta/FinRL0.py:182
↓ 2 callersMethodfill_nan_with_next_value
(ary)
ElegantRL/Beta/StockTrading.py:303
↓ 2 callersMethodfill_nan_with_next_value
(ary)
ElegantRL/Beta/beta0.py:398
↓ 2 callersMethodfinal_print
(self)
Demo_deep_learning/classify_train_mp_ring.py:489
↓ 2 callersMethodfinal_print
(self)
Demo_deep_learning/classify_train_mp.py:463
↓ 2 callersMethodfinal_print
(self)
ElegantRL/Beta/DeepLearning/classify_train_mp_ring.py:489
↓ 2 callersMethodfinal_print
(self)
ElegantRL/Beta/DeepLearning/classify_train_mp.py:463
↓ 2 callersMethodfinal_print
(self)
ElegantRL/AgentZoo/ElegantRL-MultiGPU/classify_train_mp_ring.py:489
↓ 2 callersMethodfinal_print
(self)
ElegantRL/AgentZoo/ElegantRL-MultiGPU/classify_train_mp.py:463
↓ 2 callersMethodget_action
(self, state, action_std)
ElegantRL/Beta/elegantrl/tutorial/net.py:49
↓ 2 callersMethodget_action
(self, state, action_std)
ElegantRL/Beta/elegantrl/2021-04-04 ElegantRL/tutorial/net.py:49
↓ 2 callersMethodget_action
(self, state, action_std)
ElegantRL/Beta/elegantrl/AgentZoo/ElegantRL-PER/net.py:105
↓ 2 callersMethodget_action
(self, state, action_std)
ElegantRL/AgentZoo/ElegantRL-MultiGPU/net.py:105
↓ 2 callersMethodget_action
(self, state, action_std)
ElegantRL/AgentZoo/ElegantRL-MultiGPU/tutorial/net.py:49
↓ 2 callersMethodget_action_logprob
(self, state)
ElegantRL/Beta/elegantrl/tutorial/net.py:105
↓ 2 callersMethodget_action_logprob
(self, state)
ElegantRL/Beta/elegantrl/2021-04-04 ElegantRL/tutorial/net.py:105
↓ 2 callersMethodget_action_logprob
(self, state)
ElegantRL/Beta/elegantrl2/tutorial/net.py:77
↓ 2 callersMethodget_action_logprob
(self, state)
ElegantRL/tutorial/net.py:77
↓ 2 callersMethodget_action_logprob
(self, state)
ElegantRL/AgentZoo/ElegantRL-MultiGPU/tutorial/net.py:105
↓ 2 callersFunctionget_avg_std__for_state_norm
return the state normalization data: neg_avg and div_std ReplayBuffer.print_state_norm() will print `neg_avg` and `div_std` You can save thes
ElegantRL/Beta/elegantrl2/env.py:118
↓ 2 callersFunctionget_avg_std__for_state_norm
return the state normalization data: neg_avg and div_std ReplayBuffer.print_state_norm() will print `neg_avg` and `div_std` You can save thes
ElegantRL/elegantrl2/env.py:118
↓ 2 callersFunctionget_episode_return
(env, act, device)
ElegantRL/Example_SingleFilePPO.py:587
↓ 2 callersFunctionget_episode_return
(env, actor)
ElegantRL/Beta/ceta1.py:84
↓ 2 callersFunctionget_episode_return
(env, act, device)
ElegantRL/Beta/elegantrl/2021-04-04 ElegantRL/run.py:515
↓ 2 callersFunctionget_episode_return
(env, act, device)
ElegantRL/AgentZoo/ElegantRL-MultiGPU/run.py:695
↓ 2 callersFunctionget_episode_return_and_step
(env, act, device)
ElegantRL/run.py:1075
↓ 2 callersFunctionget_episode_return_and_step
(env, act, device)
ElegantRL/Beta/run.py:1066
↓ 2 callersFunctionget_episode_return_and_step
(env, act, device)
ElegantRL/Beta/elegantrl/run.py:1064
↓ 2 callersFunctionget_episode_return_and_step
(env, act, device)
ElegantRL/Beta/elegantrl2/run.py:737
↓ 2 callersFunctionget_episode_return_and_step
(env, act, device)
ElegantRL/Beta/elegantrl2/tutorial/run.py:241
↓ 2 callersFunctionget_episode_return_and_step
(env, act, device)
ElegantRL/tutorial/run.py:241
↓ 2 callersFunctionget_episode_return_and_step
(env, act, device)
ElegantRL/elegantrl2/evaluator.py:101
↓ 2 callersFunctionget_gym_env_info
get information of a standard OpenAI gym env. The DRL algorithm AgentXXX need these env information for building networks and training. `obj
ElegantRL/env.py:257
↓ 2 callersFunctionget_gym_env_info
get information of a standard OpenAI gym env. The DRL algorithm AgentXXX need these env information for building networks and training. `obj
ElegantRL/Beta/elegantrl/env.py:259
↓ 2 callersFunctionget_gym_env_info
get information of a standard OpenAI gym env. The DRL algorithm AgentXXX need these env information for building networks and training. `obj
ElegantRL/Beta/elegantrl2/env.py:208
↓ 2 callersFunctionget_gym_env_info
get information of a standard OpenAI gym env. The DRL algorithm AgentXXX need these env information for building networks and training. `obj
ElegantRL/elegantrl2/env.py:208
↓ 2 callersMethodget_indices_is_weights
(self, batch_size, beg, end)
ElegantRL/Beta/elegantrl/2021-04-04 ElegantRL/replay.py:571
↓ 2 callersMethodget_obj_alpha
(self, logprob)
ElegantRL/net.py:170
↓ 2 callersMethodget_obj_alpha
(self, logprob)
ElegantRL/Beta/net.py:170
↓ 2 callersMethodget_obj_alpha
(self, logprob)
ElegantRL/Beta/elegantrl/net.py:170
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