这是indexloc提供的服务，不要输入任何密码
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code format and update function signatures #213

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merged 3 commits into from
Sep 12, 2020
Merged

code format and update function signatures #213

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1c7c088
code format and change function signature
Trinkle23897 Sep 12, 2020
9108084
fix bug in converter
Trinkle23897 Sep 12, 2020
bd9c837
action_range no None
Trinkle23897 Sep 12, 2020
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4 changes: 2 additions & 2 deletions examples/box2d/bipedal_hardcore_sac.py
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Original file line number Diff line number Diff line change
Expand Up @@ -119,8 +119,8 @@ def IsStop(reward):

policy = SACPolicy(
actor, actor_optim, critic1, critic1_optim, critic2, critic2_optim,
args.tau, args.gamma, args.alpha,
[env.action_space.low[0], env.action_space.high[0]],
action_range=[env.action_space.low[0], env.action_space.high[0]],
tau=args.tau, gamma=args.gamma, alpha=args.alpha,
reward_normalization=args.rew_norm,
ignore_done=args.ignore_done,
estimation_step=args.n_step)
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8 changes: 3 additions & 5 deletions examples/box2d/mcc_sac.py
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Expand Up @@ -78,14 +78,12 @@ def test_sac(args=get_args()):
target_entropy = -np.prod(env.action_space.shape)
log_alpha = torch.zeros(1, requires_grad=True, device=args.device)
alpha_optim = torch.optim.Adam([log_alpha], lr=args.alpha_lr)
alpha = (target_entropy, log_alpha, alpha_optim)
else:
alpha = args.alpha
args.alpha = (target_entropy, log_alpha, alpha_optim)

policy = SACPolicy(
actor, actor_optim, critic1, critic1_optim, critic2, critic2_optim,
args.tau, args.gamma, alpha,
[env.action_space.low[0], env.action_space.high[0]],
action_range=[env.action_space.low[0], env.action_space.high[0]],
tau=args.tau, gamma=args.gamma, alpha=args.alpha,
reward_normalization=args.rew_norm, ignore_done=True,
exploration_noise=OUNoise(0.0, args.noise_std))
# collector
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5 changes: 3 additions & 2 deletions examples/mujoco/ant_v2_ddpg.py
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Expand Up @@ -66,8 +66,9 @@ def test_ddpg(args=get_args()):
critic_optim = torch.optim.Adam(critic.parameters(), lr=args.critic_lr)
policy = DDPGPolicy(
actor, actor_optim, critic, critic_optim,
args.tau, args.gamma, GaussianNoise(sigma=args.exploration_noise),
[env.action_space.low[0], env.action_space.high[0]],
action_range=[env.action_space.low[0], env.action_space.high[0]],
tau=args.tau, gamma=args.gamma,
exploration_noise=GaussianNoise(sigma=args.exploration_noise),
reward_normalization=True, ignore_done=True)
# collector
train_collector = Collector(
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4 changes: 2 additions & 2 deletions examples/mujoco/ant_v2_sac.py
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Expand Up @@ -71,8 +71,8 @@ def test_sac(args=get_args()):
critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
policy = SACPolicy(
actor, actor_optim, critic1, critic1_optim, critic2, critic2_optim,
args.tau, args.gamma, args.alpha,
[env.action_space.low[0], env.action_space.high[0]],
action_range=[env.action_space.low[0], env.action_space.high[0]],
tau=args.tau, gamma=args.gamma, alpha=args.alpha,
reward_normalization=args.rew_norm, ignore_done=True)
# collector
train_collector = Collector(
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10 changes: 6 additions & 4 deletions examples/mujoco/ant_v2_td3.py
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Expand Up @@ -73,10 +73,12 @@ def test_td3(args=get_args()):
critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
policy = TD3Policy(
actor, actor_optim, critic1, critic1_optim, critic2, critic2_optim,
args.tau, args.gamma,
GaussianNoise(sigma=args.exploration_noise), args.policy_noise,
args.update_actor_freq, args.noise_clip,
[env.action_space.low[0], env.action_space.high[0]],
action_range=[env.action_space.low[0], env.action_space.high[0]],
tau=args.tau, gamma=args.gamma,
exploration_noise=GaussianNoise(sigma=args.exploration_noise),
policy_noise=args.policy_noise,
update_actor_freq=args.update_actor_freq,
noise_clip=args.noise_clip,
reward_normalization=True, ignore_done=True)
# collector
train_collector = Collector(
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4 changes: 2 additions & 2 deletions examples/mujoco/halfcheetahBullet_v0_sac.py
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Expand Up @@ -79,8 +79,8 @@ def test_sac(args=get_args()):
critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
policy = SACPolicy(
actor, actor_optim, critic1, critic1_optim, critic2, critic2_optim,
args.tau, args.gamma, args.alpha,
[env.action_space.low[0], env.action_space.high[0]],
action_range=[env.action_space.low[0], env.action_space.high[0]],
tau=args.tau, gamma=args.gamma, alpha=args.alpha,
reward_normalization=True, ignore_done=True)
# collector
train_collector = Collector(
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10 changes: 6 additions & 4 deletions examples/mujoco/point_maze_td3.py
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Expand Up @@ -76,10 +76,12 @@ def test_td3(args=get_args()):
critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
policy = TD3Policy(
actor, actor_optim, critic1, critic1_optim, critic2, critic2_optim,
args.tau, args.gamma,
GaussianNoise(sigma=args.exploration_noise), args.policy_noise,
args.update_actor_freq, args.noise_clip,
[env.action_space.low[0], env.action_space.high[0]],
action_range=[env.action_space.low[0], env.action_space.high[0]],
tau=args.tau, gamma=args.gamma,
exploration_noise=GaussianNoise(sigma=args.exploration_noise),
policy_noise=args.policy_noise,
update_actor_freq=args.update_actor_freq,
noise_clip=args.noise_clip,
reward_normalization=True, ignore_done=True)
# collector
train_collector = Collector(
Expand Down
5 changes: 3 additions & 2 deletions test/continuous/test_ddpg.py
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Expand Up @@ -78,8 +78,9 @@ def test_ddpg(args=get_args()):
critic_optim = torch.optim.Adam(critic.parameters(), lr=args.critic_lr)
policy = DDPGPolicy(
actor, actor_optim, critic, critic_optim,
args.tau, args.gamma, GaussianNoise(sigma=args.exploration_noise),
[env.action_space.low[0], env.action_space.high[0]],
action_range=[env.action_space.low[0], env.action_space.high[0]],
tau=args.tau, gamma=args.gamma,
exploration_noise=GaussianNoise(sigma=args.exploration_noise),
reward_normalization=args.rew_norm,
ignore_done=args.ignore_done,
estimation_step=args.n_step)
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4 changes: 2 additions & 2 deletions test/continuous/test_sac_with_il.py
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Expand Up @@ -79,8 +79,8 @@ def test_sac_with_il(args=get_args()):
critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
policy = SACPolicy(
actor, actor_optim, critic1, critic1_optim, critic2, critic2_optim,
args.tau, args.gamma, args.alpha,
[env.action_space.low[0], env.action_space.high[0]],
action_range=[env.action_space.low[0], env.action_space.high[0]],
tau=args.tau, gamma=args.gamma, alpha=args.alpha,
reward_normalization=args.rew_norm,
ignore_done=args.ignore_done,
estimation_step=args.n_step)
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9 changes: 6 additions & 3 deletions test/continuous/test_td3.py
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Expand Up @@ -82,9 +82,12 @@ def test_td3(args=get_args()):
critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
policy = TD3Policy(
actor, actor_optim, critic1, critic1_optim, critic2, critic2_optim,
args.tau, args.gamma, GaussianNoise(sigma=args.exploration_noise),
args.policy_noise, args.update_actor_freq, args.noise_clip,
[env.action_space.low[0], env.action_space.high[0]],
action_range=[env.action_space.low[0], env.action_space.high[0]],
tau=args.tau, gamma=args.gamma,
exploration_noise=GaussianNoise(sigma=args.exploration_noise),
policy_noise=args.policy_noise,
update_actor_freq=args.update_actor_freq,
noise_clip=args.noise_clip,
reward_normalization=args.rew_norm,
ignore_done=args.ignore_done,
estimation_step=args.n_step)
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18 changes: 7 additions & 11 deletions tianshou/__init__.py
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Original file line number Diff line number Diff line change
@@ -1,17 +1,13 @@
from tianshou import data, env, utils, policy, trainer, exploration

# pre-compile some common-type function-call to produce the correct benchmark
# result: https://github.com/thu-ml/tianshou/pull/193#discussion_r480536371
utils.pre_compile()


__version__ = '0.2.7'
__version__ = "0.2.7"

__all__ = [
'env',
'data',
'utils',
'policy',
'trainer',
'exploration',
"env",
"data",
"utils",
"policy",
"trainer",
"exploration",
]
21 changes: 10 additions & 11 deletions tianshou/data/__init__.py
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@@ -1,19 +1,18 @@
from tianshou.data.batch import Batch
from tianshou.data.utils.converter import to_numpy, to_torch, \
to_torch_as
from tianshou.data.utils.converter import to_numpy, to_torch, to_torch_as
from tianshou.data.utils.segtree import SegmentTree
from tianshou.data.buffer import ReplayBuffer, \
ListReplayBuffer, PrioritizedReplayBuffer
from tianshou.data.collector import Collector

__all__ = [
'Batch',
'to_numpy',
'to_torch',
'to_torch_as',
'SegmentTree',
'ReplayBuffer',
'ListReplayBuffer',
'PrioritizedReplayBuffer',
'Collector',
"Batch",
"to_numpy",
"to_torch",
"to_torch_as",
"SegmentTree",
"ReplayBuffer",
"ListReplayBuffer",
"PrioritizedReplayBuffer",
"Collector",
]
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