Functions — github.com/Yonv1943/Python

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Functions2,539 in github.com/Yonv1943/Python

⨍ Functions 2,539◇ Types & classes 504

↓ 125 callersFunctionset_dim

(i)

ElegantRL/Beta/elegantrl/AgentZoo/ElegantRL-PER/net.py:586

↓ 77 callersFunctionset_dim

(i)

ElegantRL/AgentZoo/ElegantRL-MultiGPU/net.py:562

↓ 34 callersMethodclose

(self)

Demo_deep_learning/tutorial__subp_vec_env_gym.py:121

↓ 34 callersMethodstep

(self, action)

ElegantRL/AgentZoo/ElegantRL-MultiGPU/env.py:245

↓ 31 callersMethodreset

(self)

ElegantRL/Beta/elegantrl/tutorial/env.py:15

↓ 28 callersMethodreset

(self)

ElegantRL/Beta/FinRL.py:253

↓ 28 callersMethodstep

(self, action: Tensor)

Demo_deep_learning/tutorial__subp_vec_env_gym.py:112

↓ 26 callersMethodstep

(self, action)

ElegantRL/Beta/FinRL.py:266

↓ 26 callersMethodstep

next_state, reward, done = env.step(action) convert the data type of state from float64 to float32, adjust action range to (-action_

ElegantRL/Beta/elegantrl/env.py:133

↓ 25 callersMethodstep

(self, action)

ElegantRL/Beta/elegantrl/2021-04-04 ElegantRL/env.py:301

↓ 23 callersMethodreset

(self)

ElegantRL/Beta/elegantrl2/tutorial/env.py:122

↓ 23 callersMethodstep

(self, action)

ElegantRL/Beta/elegantrl2/tutorial/env.py:136

↓ 21 callersMethodreset

state = env.reset() convert the data type of state from float64 to float32 return `array state` state.shape==(state_dim, )

ElegantRL/env.py:120

↓ 21 callersMethodstep

(self, action)

ElegantRL/Beta/elegantrl/AgentZoo/ElegantRL-PER/env.py:251

↓ 20 callersMethodstep

next_state, reward, done = env.step(action) convert the data type of state from float64 to float32, adjust action range to (-action_

ElegantRL/env.py:131

↓ 18 callersMethod__init__

(self, mid_dim, state_dim, action_dim)

ElegantRL/Beta/net.py:11

↓ 18 callersMethod__init__

(self, mid_dim, state_dim, action_dim)

ElegantRL/Beta/elegantrl/net.py:11

↓ 18 callersMethodget_q1_q2

(self, state)

ElegantRL/Beta/elegantrl/tutorial/net.py:31

↓ 17 callersMethodupdate

(self, data_idx, p)

ElegantRL/Beta/elegantrl/AgentZoo/ElegantRL-PER/agent.py:914

↓ 16 callersMethod__init__

(self, mid_dim, state_dim, action_dim)

ElegantRL/net.py:11

↓ 16 callersMethod__init__

(self, mid_dim, state_dim, action_dim)

ElegantRL/Beta/elegantrl2/net.py:11

↓ 16 callersMethod__init__

(self, mid_dim, state_dim, action_dim)

ElegantRL/elegantrl2/net.py:11

↓ 16 callersMethodoptim_update

(optimizer, objective)

ElegantRL/Example_SingleFilePPO.py:232

↓ 16 callersMethodrun

(self)

Demo_deep_learning/tutorial__sub_process_comm.py:14

↓ 15 callersMethod__init__

(self, mid_dim, state_dim, action_dim)

ElegantRL/Beta/elegantrl/2021-04-04 ElegantRL/net.py:11

↓ 15 callersMethodget_q1_q2

(self, state)

ElegantRL/Beta/elegantrl/2021-04-04 ElegantRL/net.py:53

↓ 15 callersMethodget_q1_q2

(self, state)

ElegantRL/elegantrl2/net.py:53

↓ 15 callersMethodreset

state = env.reset() convert the data type of state from float64 to float32 return `array state` state.shape==(state_dim, )

ElegantRL/Beta/elegantrl/env.py:122

↓ 15 callersMethodstep

(self, action)

ElegantRL/Beta/elegantrl/tutorial/env.py:110

↓ 15 callersMethodstep

(self, action)

ElegantRL/Beta/elegantrl/2021-04-04 ElegantRL/tutorial/env.py:110

↓ 15 callersMethodstep

(self, action)

ElegantRL/AgentZoo/ElegantRL-MultiGPU/tutorial/env.py:110

↓ 14 callersMethod__init__

(self, mid_dim, state_dim, action_dim)

ElegantRL/Beta/elegantrl/AgentZoo/ElegantRL-PER/net.py:9

↓ 14 callersMethod__init__

(self, mid_dim, state_dim, action_dim)

ElegantRL/AgentZoo/ElegantRL-MultiGPU/net.py:9

↓ 14 callersMethodget_q1_q2

(self, state)

ElegantRL/net.py:53

↓ 14 callersMethodget_q1_q2

(self, state)

ElegantRL/Beta/net.py:53

↓ 14 callersMethodget_q1_q2

(self, state)

ElegantRL/Beta/elegantrl/net.py:53

↓ 14 callersMethodget_q1_q2

(self, state)

ElegantRL/Beta/elegantrl2/net.py:53

↓ 14 callersFunctionlayer_norm

(layer, std=1.0, bias_const=1e-6)

ElegantRL/Beta/elegantrl/AgentZoo/ElegantRL-PER/net.py:577

↓ 14 callersFunctionlayer_norm

(layer, std=1.0, bias_const=1e-6)

ElegantRL/AgentZoo/ElegantRL-MultiGPU/net.py:553

↓ 14 callersFunctionnn_linear_bn

(inp_dim, out_dim, bias)

Demo_deep_learning/classify_network.py:323

↓ 14 callersFunctionnn_linear_bn

(inp_dim, out_dim, bias)

ElegantRL/Beta/DeepLearning/classify_network.py:323

↓ 14 callersFunctionnn_linear_bn

(inp_dim, out_dim, bias)

ElegantRL/AgentZoo/ElegantRL-MultiGPU/classify_network.py:323

↓ 14 callersMethodreset

(self)

Demo_deep_learning/tutorial__subp_vec_env_gym.py:105

↓ 14 callersMethodsample_batch

randomly sample a batch of data for training :int batch_size: the number of data in a batch for Stochastic Gradient Descent :retur

ElegantRL/elegantrl2/replay.py:70

↓ 13 callersMethod__init__

(self)

ElegantRL/elegantrl2/agent.py:159

↓ 13 callersMethodoptim_update

(optimizer, objective)

ElegantRL/elegantrl2/agent.py:99

↓ 13 callersMethodupdate_now_len_before_sample

update the a pointer `now_len`, which is the current data number of ReplayBuffer

ElegantRL/AgentZoo/ElegantRL-MultiGPU/agent.py:1087

↓ 12 callersMethod__init__

(self, memo_len)

ElegantRL/Beta/elegantrl/AgentZoo/ElegantRL-PER/agent.py:908

↓ 12 callersMethodget_q1_q2

(self, state)

ElegantRL/AgentZoo/ElegantRL-MultiGPU/net.py:51

↓ 12 callersFunctionlayer_norm

(layer, std=1.0, bias_const=1e-6)

ElegantRL/Beta/elegantrl/2021-04-04 ElegantRL/net.py:606

↓ 12 callersMethodreset

(self)

ElegantRL/Beta/elegantrl/2021-04-04 ElegantRL/env.py:285

↓ 12 callersMethodreset

(self)

ElegantRL/AgentZoo/ElegantRL-MultiGPU/env.py:229

↓ 12 callersMethodsample_batch

randomly sample a batch of data for training :int batch_size: the number of data in a batch for Stochastic Gradient Descent :retur

ElegantRL/Beta/elegantrl/2021-04-04 ElegantRL/replay.py:319

↓ 12 callersMethodupdate__now_len__before_sample

(self)

ElegantRL/Beta/elegantrl/AgentZoo/ElegantRL-PER/agent.py:1083

↓ 12 callersMethodupdate_now_len

update the a pointer `now_len`, which is the current data number of ReplayBuffer

ElegantRL/elegantrl2/replay.py:101

↓ 12 callersMethodupdate_now_len_before_sample

update the a pointer `now_len`, which is the current data number of ReplayBuffer

ElegantRL/Beta/elegantrl/2021-04-04 ElegantRL/replay.py:369

↓ 11 callersMethod__init__

(self)

ElegantRL/Beta/elegantrl/2021-04-04 ElegantRL/agent.py:124

↓ 11 callersMethod__init__

(self)

ElegantRL/AgentZoo/ElegantRL-MultiGPU/agent.py:159

↓ 11 callersFunctionlayer_norm

(layer, std=1.0, bias_const=1e-6)

ElegantRL/Beta/net.py:678

↓ 11 callersFunctionlayer_norm

(layer, std=1.0, bias_const=1e-6)

ElegantRL/Beta/elegantrl/net.py:678

↓ 11 callersFunctionlayer_norm

(layer, std=1.0, bias_const=1e-6)

ElegantRL/Beta/elegantrl2/net.py:596

↓ 11 callersMethodrandom_sample

(self, batch_size)

ElegantRL/Beta/elegantrl/AgentZoo/ElegantRL-PER/agent.py:1035

↓ 11 callersMethodreset

(self)

ElegantRL/Beta/elegantrl/AgentZoo/ElegantRL-PER/env.py:235

↓ 11 callersMethodsample_batch

randomly sample a batch of data for training :int batch_size: the number of data in a batch for Stochastic Gradient Descent :retur

ElegantRL/replay.py:86

↓ 11 callersMethodsample_batch

randomly sample a batch of data for training :int batch_size: the number of data in a batch for Stochastic Gradient Descent :retur

ElegantRL/Beta/replay.py:73

↓ 11 callersMethodsample_batch

randomly sample a batch of data for training :int batch_size: the number of data in a batch for Stochastic Gradient Descent :retur

ElegantRL/Beta/elegantrl/replay.py:73

↓ 11 callersMethodsample_batch

randomly sample a batch of data for training :int batch_size: the number of data in a batch for Stochastic Gradient Descent :retur

ElegantRL/Beta/elegantrl2/replay.py:73

↓ 11 callersFunctionsoft_target_update

(target, current, tau=2 ** -8)

ElegantRL/Beta/elegantrl/AgentZoo/ElegantRL-PER/agent.py:1197

↓ 10 callersFunctionlayer_norm

(layer, std=1.0, bias_const=1e-6)

ElegantRL/net.py:606

↓ 10 callersMethodsoft_update

soft update a target network via current network `nn.Module target_net` target network update via a current network, it is more stable

ElegantRL/elegantrl2/agent.py:119

↓ 10 callersMethodsoft_update

soft update a target network via current network :nn.Module target_net: target network update via a current network, it is more stable

ElegantRL/AgentZoo/ElegantRL-MultiGPU/agent.py:145

↓ 10 callersFunctiontrain_and_evaluate_mp

(args)

ElegantRL/elegantrl2/run.py:561

↓ 9 callersMethod__init__

(self)

ElegantRL/agent.py:157

↓ 9 callersMethod__init__

(self)

ElegantRL/Beta/agent.py:159

↓ 9 callersMethod__init__

(self)

ElegantRL/Beta/elegantrl/agent.py:157

↓ 9 callersMethod__init__

(self)

ElegantRL/Beta/elegantrl2/agent.py:161

↓ 9 callersFunctionlayer_norm

(layer, std=1.0, bias_const=1e-6)

ElegantRL/elegantrl2/net.py:596

↓ 9 callersMethodoptim_update

(optimizer, objective)

ElegantRL/agent.py:96

↓ 9 callersMethodoptim_update

(optimizer, objective)

ElegantRL/Beta/agent.py:98

↓ 9 callersMethodoptim_update

(optimizer, objective)

ElegantRL/Beta/elegantrl/agent.py:96

↓ 9 callersMethodoptim_update

(optimizer, objective)

ElegantRL/Beta/elegantrl2/agent.py:100

↓ 9 callersMethodreset

(self)

ElegantRL/Beta/elegantrl/2021-04-04 ElegantRL/tutorial/env.py:15

↓ 9 callersMethodreset

(self)

ElegantRL/tutorial/env.py:122

↓ 9 callersMethodreset

(self)

ElegantRL/AgentZoo/ElegantRL-MultiGPU/tutorial/env.py:15

↓ 9 callersMethodsample_batch

randomly sample a batch of data for training :int batch_size: the number of data in a batch for Stochastic Gradient Descent :retur

ElegantRL/AgentZoo/ElegantRL-MultiGPU/agent.py:1052

↓ 9 callersMethodstep

(self, action)

ElegantRL/tutorial/env.py:136

↓ 9 callersFunctiontrain_and_evaluate_mg

(args)

ElegantRL/Beta/elegantrl2/run.py:562

↓ 8 callersMethod__init__

(self, inp_dim=3, mid_dim=128, glb_size=6)

Demo_deep_learning/classify_network.py:48

↓ 8 callersMethod__init__

(self, inp_dim=3, mid_dim=128, glb_size=6)

ElegantRL/Beta/DeepLearning/classify_network.py:48

↓ 8 callersMethod__init__

(self, mid_dim, state_dim, action_dim)

ElegantRL/Beta/elegantrl2/tutorial/net.py:7

↓ 8 callersMethod__init__

(self, mid_dim, state_dim, action_dim)

ElegantRL/tutorial/net.py:7

↓ 8 callersMethod__init__

(self, inp_dim=3, mid_dim=128, glb_size=6)

ElegantRL/AgentZoo/ElegantRL-MultiGPU/classify_network.py:48

↓ 8 callersMethodsave_or_load_agent

(self, cwd, if_save)

ElegantRL/elegantrl2/agent.py:128

↓ 8 callersMethodsoft_update

soft update a target network via current network `nn.Module target_net` target network update via a current network, it is more stable

ElegantRL/agent.py:116

↓ 8 callersMethodsoft_update

(target_net, current_net, tau)

ElegantRL/Example_SingleFilePPO.py:238

↓ 8 callersMethodsoft_update

soft update a target network via current network `nn.Module target_net` target network update via a current network, it is more stable

ElegantRL/Beta/agent.py:118

↓ 8 callersMethodsoft_update

soft update a target network via current network `nn.Module target_net` target network update via a current network, it is more stable

ElegantRL/Beta/elegantrl/agent.py:116

↓ 8 callersMethodsoft_update

soft update a target network via current network :nn.Module target_net: target network update via a current network, it is more stable

ElegantRL/Beta/elegantrl/2021-04-04 ElegantRL/agent.py:110

↓ 8 callersMethodsoft_update

soft update a target network via current network `nn.Module target_net` target network update via a current network, it is more stable

ElegantRL/Beta/elegantrl2/agent.py:120

↓ 8 callersFunctionto_images

(ary, )

ElegantRL/Beta/DeepLearning/yonv_utils.py:15

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