sac mujoco result (#246)
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@ -58,8 +58,8 @@ def test_dqn(args=get_args()):
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args.state_shape = env.observation_space.shape or env.observation_space.n
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args.action_shape = env.env.action_space.shape or env.env.action_space.n
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# should be N_FRAMES x H x W
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print("Observations shape: ", args.state_shape)
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print("Actions shape: ", args.action_shape)
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print("Observations shape:", args.state_shape)
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print("Actions shape:", args.action_shape)
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# make environments
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train_envs = SubprocVectorEnv([lambda: make_atari_env(args)
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for _ in range(args.training_num)])
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@ -79,7 +79,9 @@ def test_dqn(args=get_args()):
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target_update_freq=args.target_update_freq)
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# load a previous policy
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if args.resume_path:
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policy.load_state_dict(torch.load(args.resume_path))
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policy.load_state_dict(torch.load(
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args.resume_path, map_location=args.device
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))
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print("Loaded agent from: ", args.resume_path)
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# replay buffer: `save_last_obs` and `stack_num` can be removed together
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# when you have enough RAM
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@ -1,6 +1,6 @@
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# Bipedal-Hardcore-SAC
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- Our default choice: remove the done flag penalty, will soon converge to \~270 reward within 100 epochs (10M env steps, 3~4 hours, see the image below)
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- Our default choice: remove the done flag penalty, will soon converge to \~280 reward within 100 epochs (10M env steps, 3~4 hours, see the image below)
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- If the done penalty is not removed, it converges much slower than before, about 200 epochs (20M env steps) to reach the same performance (\~200 reward)
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@ -6,11 +6,11 @@ import argparse
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import numpy as np
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from torch.utils.tensorboard import SummaryWriter
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from tianshou.policy import SACPolicy
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from tianshou.utils.net.common import Net
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from tianshou.env import SubprocVectorEnv
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from tianshou.trainer import offpolicy_trainer
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from tianshou.data import Collector, ReplayBuffer
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from tianshou.policy import SACPolicy
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from tianshou.utils.net.common import Net
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from tianshou.utils.net.continuous import ActorProb, Critic
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@ -24,8 +24,8 @@ def get_args():
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parser.add_argument('--gamma', type=float, default=0.99)
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parser.add_argument('--tau', type=float, default=0.005)
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parser.add_argument('--alpha', type=float, default=0.1)
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parser.add_argument('--auto_alpha', type=int, default=1)
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parser.add_argument('--alpha_lr', type=float, default=3e-4)
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parser.add_argument('--auto-alpha', type=int, default=1)
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parser.add_argument('--alpha-lr', type=float, default=3e-4)
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parser.add_argument('--epoch', type=int, default=100)
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parser.add_argument('--step-per-epoch', type=int, default=10000)
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parser.add_argument('--collect-per-step', type=int, default=10)
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@ -35,54 +35,50 @@ def get_args():
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parser.add_argument('--test-num', type=int, default=100)
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parser.add_argument('--logdir', type=str, default='log')
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parser.add_argument('--render', type=float, default=0.)
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parser.add_argument('--rew-norm', type=int, default=0)
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parser.add_argument('--ignore-done', type=int, default=0)
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parser.add_argument('--n-step', type=int, default=4)
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parser.add_argument(
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'--device', type=str,
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default='cuda' if torch.cuda.is_available() else 'cpu')
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parser.add_argument('--resume_path', type=str, default=None)
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parser.add_argument('--resume-path', type=str, default=None)
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return parser.parse_args()
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class EnvWrapper(object):
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"""Env wrapper for reward scale, action repeat and action noise"""
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class Wrapper(gym.Wrapper):
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"""Env wrapper for reward scale, action repeat and removing done penalty"""
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def __init__(self, task, action_repeat=3, reward_scale=5, act_noise=0.0):
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self._env = gym.make(task)
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def __init__(self, env, action_repeat=3, reward_scale=5, rm_done=True):
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super().__init__(env)
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self.action_repeat = action_repeat
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self.reward_scale = reward_scale
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self.act_noise = act_noise
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def __getattr__(self, name):
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return getattr(self._env, name)
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self.rm_done = rm_done
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def step(self, action):
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# add action noise
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action += self.act_noise * (-2 * np.random.random(4) + 1)
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r = 0.0
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for _ in range(self.action_repeat):
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obs_, reward_, done_, info_ = self._env.step(action)
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obs, reward, done, info = self.env.step(action)
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# remove done reward penalty
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if done_:
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if not done or not self.rm_done:
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r = r + reward
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if done:
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break
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r = r + reward_
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# scale reward
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return obs_, self.reward_scale * r, done_, info_
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return obs, self.reward_scale * r, done, info
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def test_sac_bipedal(args=get_args()):
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env = EnvWrapper(args.task)
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env = Wrapper(gym.make(args.task))
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args.state_shape = env.observation_space.shape or env.observation_space.n
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args.action_shape = env.action_space.shape or env.action_space.n
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args.max_action = env.action_space.high[0]
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train_envs = SubprocVectorEnv(
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[lambda: EnvWrapper(args.task) for _ in range(args.training_num)])
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train_envs = SubprocVectorEnv([
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lambda: Wrapper(gym.make(args.task))
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for _ in range(args.training_num)])
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# test_envs = gym.make(args.task)
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test_envs = SubprocVectorEnv([lambda: EnvWrapper(args.task, reward_scale=1)
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for _ in range(args.test_num)])
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test_envs = SubprocVectorEnv([
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lambda: Wrapper(gym.make(args.task), reward_scale=1, rm_done=False)
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for _ in range(args.test_num)])
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# seed
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np.random.seed(args.seed)
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@ -117,8 +113,6 @@ def test_sac_bipedal(args=get_args()):
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actor, actor_optim, critic1, critic1_optim, critic2, critic2_optim,
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action_range=[env.action_space.low[0], env.action_space.high[0]],
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tau=args.tau, gamma=args.gamma, alpha=args.alpha,
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reward_normalization=args.rew_norm,
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ignore_done=args.ignore_done,
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estimation_step=args.n_step)
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# load a previous policy
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if args.resume_path:
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@ -67,11 +67,13 @@ def test_sac(args=get_args()):
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args.max_action, args.device, unbounded=True
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).to(args.device)
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actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
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net = Net(args.layer_num, args.state_shape,
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args.action_shape, concat=True, device=args.device)
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critic1 = Critic(net, args.device).to(args.device)
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net_c1 = Net(args.layer_num, args.state_shape,
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args.action_shape, concat=True, device=args.device)
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critic1 = Critic(net_c1, args.device).to(args.device)
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critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
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critic2 = Critic(net, args.device).to(args.device)
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net_c2 = Net(args.layer_num, args.state_shape,
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args.action_shape, concat=True, device=args.device)
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critic2 = Critic(net_c2, args.device).to(args.device)
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critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
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if args.auto_alpha:
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@ -1,3 +1,27 @@
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Result of Ant-v2:
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# Mujoco Result
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## SAC (single run)
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The best reward computes from 100 episodes returns in the test phase.
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SAC on Swimmer-v3 always stops at 47\~48.
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| task | 3M best reward | parameters | time cost (3M) |
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| -------------- | ----------------- | ------------------------------------------------------- | -------------- |
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| HalfCheetah-v3 | 10157.70 ± 171.70 | `python3 mujoco_sac.py --task HalfCheetah-v3` | 2~3h |
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| Walker2d-v3 | 5143.04 ± 15.57 | `python3 mujoco_sac.py --task Walker2d-v3` | 2~3h |
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| Hopper-v3 | 3604.19 ± 169.55 | `python3 mujoco_sac.py --task Hopper-v3` | 2~3h |
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| Humanoid-v3 | 6579.20 ± 1470.57 | `python3 mujoco_sac.py --task Humanoid-v3 --alpha 0.05` | 2~3h |
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| Ant-v3 | 6281.65 ± 686.28 | `python3 mujoco_sac.py --task Ant-v3` | 2~3h |
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### Which parts are important?
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0. DO NOT share the same network with two critic networks.
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1. The sigma (of the Gaussian policy) MUST be conditioned on input.
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2. The network size should not be less than 256.
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3. The deterministic evaluation helps a lot :)
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@ -16,27 +16,36 @@ from tianshou.utils.net.continuous import ActorProb, Critic
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def get_args():
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parser = argparse.ArgumentParser()
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parser.add_argument('--task', type=str, default='Ant-v2')
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parser.add_argument('--task', type=str, default='Ant-v3')
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parser.add_argument('--seed', type=int, default=1626)
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parser.add_argument('--buffer-size', type=int, default=20000)
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parser.add_argument('--buffer-size', type=int, default=1000000)
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parser.add_argument('--actor-lr', type=float, default=3e-4)
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parser.add_argument('--critic-lr', type=float, default=1e-3)
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parser.add_argument('--critic-lr', type=float, default=3e-4)
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parser.add_argument('--gamma', type=float, default=0.99)
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parser.add_argument('--tau', type=float, default=0.005)
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parser.add_argument('--alpha', type=float, default=0.2)
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parser.add_argument('--auto-alpha', default=False, action='store_true')
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parser.add_argument('--alpha-lr', type=float, default=3e-4)
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parser.add_argument('--n-step', type=int, default=2)
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parser.add_argument('--epoch', type=int, default=100)
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parser.add_argument('--step-per-epoch', type=int, default=2400)
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parser.add_argument('--collect-per-step', type=int, default=10)
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parser.add_argument('--batch-size', type=int, default=128)
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parser.add_argument('--step-per-epoch', type=int, default=10000)
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parser.add_argument('--collect-per-step', type=int, default=4)
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parser.add_argument('--update-per-step', type=int, default=1)
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parser.add_argument('--pre-collect-step', type=int, default=10000)
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parser.add_argument('--batch-size', type=int, default=256)
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parser.add_argument('--hidden-layer-size', type=int, default=256)
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parser.add_argument('--layer-num', type=int, default=1)
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parser.add_argument('--training-num', type=int, default=8)
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parser.add_argument('--training-num', type=int, default=16)
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parser.add_argument('--test-num', type=int, default=100)
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parser.add_argument('--logdir', type=str, default='log')
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parser.add_argument('--render', type=float, default=0.)
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parser.add_argument('--rew-norm', type=bool, default=True)
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parser.add_argument('--log-interval', type=int, default=1000)
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parser.add_argument(
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'--device', type=str,
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default='cuda' if torch.cuda.is_available() else 'cpu')
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parser.add_argument('--resume-path', type=str, default=None)
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parser.add_argument('--watch', default=False, action='store_true',
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help='watch the play of pre-trained policy only')
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return parser.parse_args()
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@ -45,6 +54,10 @@ def test_sac(args=get_args()):
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args.state_shape = env.observation_space.shape or env.observation_space.n
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args.action_shape = env.action_space.shape or env.action_space.n
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args.max_action = env.action_space.high[0]
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print("Observations shape:", args.state_shape)
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print("Actions shape:", args.action_shape)
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print("Action range:", np.min(env.action_space.low),
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np.max(env.action_space.high))
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# train_envs = gym.make(args.task)
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train_envs = SubprocVectorEnv(
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[lambda: gym.make(args.task) for _ in range(args.training_num)])
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@ -57,53 +70,84 @@ def test_sac(args=get_args()):
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train_envs.seed(args.seed)
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test_envs.seed(args.seed)
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# model
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net = Net(args.layer_num, args.state_shape, device=args.device)
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net = Net(args.layer_num, args.state_shape, device=args.device,
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hidden_layer_size=args.hidden_layer_size)
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actor = ActorProb(
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net, args.action_shape,
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args.max_action, args.device, unbounded=True
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net, args.action_shape, args.max_action, args.device, unbounded=True,
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hidden_layer_size=args.hidden_layer_size, conditioned_sigma=True,
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).to(args.device)
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actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
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net = Net(args.layer_num, args.state_shape,
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args.action_shape, concat=True, device=args.device)
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critic1 = Critic(net, args.device).to(args.device)
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net_c1 = Net(args.layer_num, args.state_shape, args.action_shape,
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concat=True, device=args.device,
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hidden_layer_size=args.hidden_layer_size)
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critic1 = Critic(
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net_c1, args.device, hidden_layer_size=args.hidden_layer_size
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).to(args.device)
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critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
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critic2 = Critic(net, args.device).to(args.device)
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net_c2 = Net(args.layer_num, args.state_shape, args.action_shape,
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concat=True, device=args.device,
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hidden_layer_size=args.hidden_layer_size)
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critic2 = Critic(
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net_c2, args.device, hidden_layer_size=args.hidden_layer_size
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).to(args.device)
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critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
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if args.auto_alpha:
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target_entropy = -np.prod(env.action_space.shape)
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log_alpha = torch.zeros(1, requires_grad=True, device=args.device)
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alpha_optim = torch.optim.Adam([log_alpha], lr=args.alpha_lr)
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args.alpha = (target_entropy, log_alpha, alpha_optim)
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policy = SACPolicy(
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actor, actor_optim, critic1, critic1_optim, critic2, critic2_optim,
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action_range=[env.action_space.low[0], env.action_space.high[0]],
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tau=args.tau, gamma=args.gamma, alpha=args.alpha,
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reward_normalization=args.rew_norm, ignore_done=True)
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estimation_step=args.n_step)
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# load a previous policy
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if args.resume_path:
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policy.load_state_dict(torch.load(
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args.resume_path, map_location=args.device
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))
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print("Loaded agent from: ", args.resume_path)
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# collector
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train_collector = Collector(
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policy, train_envs, ReplayBuffer(args.buffer_size))
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test_collector = Collector(policy, test_envs)
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# train_collector.collect(n_step=args.buffer_size)
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# log
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log_path = os.path.join(args.logdir, args.task, 'sac')
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writer = SummaryWriter(log_path)
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def save_fn(policy):
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torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))
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def stop_fn(mean_rewards):
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return mean_rewards >= env.spec.reward_threshold
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# trainer
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result = offpolicy_trainer(
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policy, train_collector, test_collector, args.epoch,
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args.step_per_epoch, args.collect_per_step, args.test_num,
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args.batch_size, stop_fn=stop_fn, save_fn=save_fn, writer=writer)
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assert stop_fn(result['best_reward'])
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if __name__ == '__main__':
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pprint.pprint(result)
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# Let's watch its performance!
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def watch():
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# watch agent's performance
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print("Testing agent ...")
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policy.eval()
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test_envs.seed(args.seed)
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test_collector.reset()
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result = test_collector.collect(n_episode=[1] * args.test_num,
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render=args.render)
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print(f'Final reward: {result["rew"]}, length: {result["len"]}')
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pprint.pprint(result)
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def save_fn(policy):
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torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))
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def stop_fn(mean_rewards):
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return False
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if args.watch:
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watch()
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exit(0)
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# trainer
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train_collector.collect(n_step=args.pre_collect_step, random=True)
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result = offpolicy_trainer(
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policy, train_collector, test_collector, args.epoch,
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args.step_per_epoch, args.collect_per_step, args.test_num,
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args.batch_size, args.update_per_step,
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stop_fn=stop_fn, save_fn=save_fn, writer=writer,
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log_interval=args.log_interval)
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pprint.pprint(result)
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watch()
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if __name__ == '__main__':
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BIN
examples/mujoco/results/sac/all.png
Normal file
BIN
examples/mujoco/results/sac/all.png
Normal file
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@ -71,6 +71,8 @@ def test_sac(args=get_args()):
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args.action_shape, concat=True, device=args.device)
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critic1 = Critic(net, args.device).to(args.device)
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critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
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net = Net(args.layer_num, args.state_shape,
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args.action_shape, concat=True, device=args.device)
|
||||
critic2 = Critic(net, args.device).to(args.device)
|
||||
critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
|
||||
policy = SACPolicy(
|
||||
@ -72,6 +72,8 @@ def test_td3(args=get_args()):
|
||||
args.action_shape, concat=True, device=args.device)
|
||||
critic1 = Critic(net, args.device).to(args.device)
|
||||
critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
|
||||
net = Net(args.layer_num, args.state_shape,
|
||||
args.action_shape, concat=True, device=args.device)
|
||||
critic2 = Critic(net, args.device).to(args.device)
|
||||
critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
|
||||
policy = TD3Policy(
|
||||
@ -70,11 +70,13 @@ def test_sac_with_il(args=get_args()):
|
||||
net, args.action_shape, args.max_action, args.device, unbounded=True
|
||||
).to(args.device)
|
||||
actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
|
||||
net = Net(args.layer_num, args.state_shape,
|
||||
args.action_shape, concat=True, device=args.device)
|
||||
critic1 = Critic(net, args.device).to(args.device)
|
||||
net_c1 = Net(args.layer_num, args.state_shape,
|
||||
args.action_shape, concat=True, device=args.device)
|
||||
critic1 = Critic(net_c1, args.device).to(args.device)
|
||||
critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
|
||||
critic2 = Critic(net, args.device).to(args.device)
|
||||
net_c2 = Net(args.layer_num, args.state_shape,
|
||||
args.action_shape, concat=True, device=args.device)
|
||||
critic2 = Critic(net_c2, args.device).to(args.device)
|
||||
critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
|
||||
policy = SACPolicy(
|
||||
actor, actor_optim, critic1, critic1_optim, critic2, critic2_optim,
|
||||
|
||||
@ -74,11 +74,13 @@ def test_td3(args=get_args()):
|
||||
args.max_action, args.device
|
||||
).to(args.device)
|
||||
actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
|
||||
net = Net(args.layer_num, args.state_shape,
|
||||
args.action_shape, concat=True, device=args.device)
|
||||
critic1 = Critic(net, args.device).to(args.device)
|
||||
net_c1 = Net(args.layer_num, args.state_shape,
|
||||
args.action_shape, concat=True, device=args.device)
|
||||
critic1 = Critic(net_c1, args.device).to(args.device)
|
||||
critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
|
||||
critic2 = Critic(net, args.device).to(args.device)
|
||||
net_c2 = Net(args.layer_num, args.state_shape,
|
||||
args.action_shape, concat=True, device=args.device)
|
||||
critic2 = Critic(net_c2, args.device).to(args.device)
|
||||
critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
|
||||
policy = TD3Policy(
|
||||
actor, actor_optim, critic1, critic1_optim, critic2, critic2_optim,
|
||||
|
||||
@ -62,11 +62,11 @@ def test_discrete_sac(args=get_args()):
|
||||
net = Net(args.layer_num, args.state_shape, device=args.device)
|
||||
actor = Actor(net, args.action_shape, softmax_output=False).to(args.device)
|
||||
actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
|
||||
net = Net(args.layer_num, args.state_shape, device=args.device)
|
||||
critic1 = Critic(net, last_size=args.action_shape).to(args.device)
|
||||
net_c1 = Net(args.layer_num, args.state_shape, device=args.device)
|
||||
critic1 = Critic(net_c1, last_size=args.action_shape).to(args.device)
|
||||
critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
|
||||
net = Net(args.layer_num, args.state_shape, device=args.device)
|
||||
critic2 = Critic(net, last_size=args.action_shape).to(args.device)
|
||||
net_c2 = Net(args.layer_num, args.state_shape, device=args.device)
|
||||
critic2 = Critic(net_c2, last_size=args.action_shape).to(args.device)
|
||||
critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
|
||||
|
||||
# better not to use auto alpha in CartPole
|
||||
|
||||
@ -38,6 +38,9 @@ class SACPolicy(DDPGPolicy):
|
||||
defaults to False.
|
||||
:param BaseNoise exploration_noise: add a noise to action for exploration,
|
||||
defaults to None. This is useful when solving hard-exploration problem.
|
||||
:param bool deterministic_eval: whether to use deterministic action (mean
|
||||
of Gaussian policy) instead of stochastic action sampled by the policy,
|
||||
defaults to True.
|
||||
|
||||
.. seealso::
|
||||
|
||||
@ -63,6 +66,7 @@ class SACPolicy(DDPGPolicy):
|
||||
ignore_done: bool = False,
|
||||
estimation_step: int = 1,
|
||||
exploration_noise: Optional[BaseNoise] = None,
|
||||
deterministic_eval: bool = True,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
super().__init__(None, None, None, None, action_range, tau, gamma,
|
||||
@ -86,6 +90,7 @@ class SACPolicy(DDPGPolicy):
|
||||
else:
|
||||
self._alpha = alpha
|
||||
|
||||
self._deterministic_eval = deterministic_eval
|
||||
self.__eps = np.finfo(np.float32).eps.item()
|
||||
|
||||
def train(self, mode: bool = True) -> "SACPolicy":
|
||||
@ -116,13 +121,16 @@ class SACPolicy(DDPGPolicy):
|
||||
logits, h = self.actor(obs, state=state, info=batch.info)
|
||||
assert isinstance(logits, tuple)
|
||||
dist = Independent(Normal(*logits), 1)
|
||||
x = dist.rsample()
|
||||
if self._deterministic_eval and not self.training:
|
||||
x = logits[0]
|
||||
else:
|
||||
x = dist.rsample()
|
||||
y = torch.tanh(x)
|
||||
act = y * self._action_scale + self._action_bias
|
||||
y = self._action_scale * (1 - y.pow(2)) + self.__eps
|
||||
log_prob = dist.log_prob(x).unsqueeze(-1)
|
||||
log_prob = log_prob - torch.log(y).sum(-1, keepdim=True)
|
||||
if self._noise is not None and not self.updating:
|
||||
if self._noise is not None and self.training and not self.updating:
|
||||
act += to_torch_as(self._noise(act.shape), act)
|
||||
act = act.clamp(self._range[0], self._range[1])
|
||||
return Batch(
|
||||
|
||||
@ -6,6 +6,10 @@ from typing import Any, Dict, Tuple, Union, Optional, Sequence
|
||||
from tianshou.data import to_torch, to_torch_as
|
||||
|
||||
|
||||
SIGMA_MIN = -20
|
||||
SIGMA_MAX = 2
|
||||
|
||||
|
||||
class Actor(nn.Module):
|
||||
"""Simple actor network with MLP.
|
||||
|
||||
@ -89,12 +93,17 @@ class ActorProb(nn.Module):
|
||||
device: Union[str, int, torch.device] = "cpu",
|
||||
unbounded: bool = False,
|
||||
hidden_layer_size: int = 128,
|
||||
conditioned_sigma: bool = False,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.preprocess = preprocess_net
|
||||
self.device = device
|
||||
self.mu = nn.Linear(hidden_layer_size, np.prod(action_shape))
|
||||
self.sigma = nn.Parameter(torch.zeros(np.prod(action_shape), 1))
|
||||
self._c_sigma = conditioned_sigma
|
||||
if conditioned_sigma:
|
||||
self.sigma = nn.Linear(hidden_layer_size, np.prod(action_shape))
|
||||
else:
|
||||
self.sigma = nn.Parameter(torch.zeros(np.prod(action_shape), 1))
|
||||
self._max = max_action
|
||||
self._unbounded = unbounded
|
||||
|
||||
@ -109,9 +118,14 @@ class ActorProb(nn.Module):
|
||||
mu = self.mu(logits)
|
||||
if not self._unbounded:
|
||||
mu = self._max * torch.tanh(mu)
|
||||
shape = [1] * len(mu.shape)
|
||||
shape[1] = -1
|
||||
sigma = (self.sigma.view(shape) + torch.zeros_like(mu)).exp()
|
||||
if self._c_sigma:
|
||||
sigma = torch.clamp(
|
||||
self.sigma(logits), min=SIGMA_MIN, max=SIGMA_MAX
|
||||
).exp()
|
||||
else:
|
||||
shape = [1] * len(mu.shape)
|
||||
shape[1] = -1
|
||||
sigma = (self.sigma.view(shape) + torch.zeros_like(mu)).exp()
|
||||
return (mu, sigma), state
|
||||
|
||||
|
||||
@ -131,6 +145,7 @@ class RecurrentActorProb(nn.Module):
|
||||
device: Union[str, int, torch.device] = "cpu",
|
||||
unbounded: bool = False,
|
||||
hidden_layer_size: int = 128,
|
||||
conditioned_sigma: bool = False,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.device = device
|
||||
@ -141,7 +156,11 @@ class RecurrentActorProb(nn.Module):
|
||||
batch_first=True,
|
||||
)
|
||||
self.mu = nn.Linear(hidden_layer_size, np.prod(action_shape))
|
||||
self.sigma = nn.Parameter(torch.zeros(np.prod(action_shape), 1))
|
||||
self._c_sigma = conditioned_sigma
|
||||
if conditioned_sigma:
|
||||
self.sigma = nn.Linear(hidden_layer_size, np.prod(action_shape))
|
||||
else:
|
||||
self.sigma = nn.Parameter(torch.zeros(np.prod(action_shape), 1))
|
||||
self._max = max_action
|
||||
self._unbounded = unbounded
|
||||
|
||||
@ -170,9 +189,14 @@ class RecurrentActorProb(nn.Module):
|
||||
mu = self.mu(logits)
|
||||
if not self._unbounded:
|
||||
mu = self._max * torch.tanh(mu)
|
||||
shape = [1] * len(mu.shape)
|
||||
shape[1] = -1
|
||||
sigma = (self.sigma.view(shape) + torch.zeros_like(mu)).exp()
|
||||
if self._c_sigma:
|
||||
sigma = torch.clamp(
|
||||
self.sigma(logits), min=SIGMA_MIN, max=SIGMA_MAX
|
||||
).exp()
|
||||
else:
|
||||
shape = [1] * len(mu.shape)
|
||||
shape[1] = -1
|
||||
sigma = (self.sigma.view(shape) + torch.zeros_like(mu)).exp()
|
||||
# please ensure the first dim is batch size: [bsz, len, ...]
|
||||
return (mu, sigma), {"h": h.transpose(0, 1).detach(),
|
||||
"c": c.transpose(0, 1).detach()}
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user