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This commit is contained in:
JohnJim0816
2021-03-28 11:18:52 +08:00
parent 2df8d965d2
commit 6e4d966e1f
56 changed files with 497 additions and 165 deletions
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10
codes/DDPG/agent.py
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@@ -19,12 +19,12 @@ from common.memory import ReplayBuffer
class DDPG:
def __init__(self, n_states, n_actions, cfg):
def __init__(self, state_dim, action_dim, cfg):
self.device = cfg.device
self.critic = Critic(n_states, n_actions, cfg.hidden_dim).to(cfg.device)
self.actor = Actor(n_states, n_actions, cfg.hidden_dim).to(cfg.device)
self.target_critic = Critic(n_states, n_actions, cfg.hidden_dim).to(cfg.device)
self.target_actor = Actor(n_states, n_actions, cfg.hidden_dim).to(cfg.device)
self.critic = Critic(state_dim, action_dim, cfg.hidden_dim).to(cfg.device)
self.actor = Actor(state_dim, action_dim, cfg.hidden_dim).to(cfg.device)
self.target_critic = Critic(state_dim, action_dim, cfg.hidden_dim).to(cfg.device)
self.target_actor = Actor(state_dim, action_dim, cfg.hidden_dim).to(cfg.device)
for target_param, param in zip(self.target_critic.parameters(), self.critic.parameters()):
target_param.data.copy_(param.data)

6
codes/DDPG/env.py
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@@ -41,17 +41,17 @@ class OUNoise(object):
self.max_sigma = max_sigma
self.min_sigma = min_sigma
self.decay_period = decay_period
self.n_actions = action_space.shape[0]
self.action_dim = action_space.shape[0]
self.low = action_space.low
self.high = action_space.high
self.reset()
def reset(self):
self.obs = np.ones(self.n_actions) * self.mu
self.obs = np.ones(self.action_dim) * self.mu
def evolve_obs(self):
x = self.obs
dx = self.theta * (self.mu - x) + self.sigma * np.random.randn(self.n_actions)
dx = self.theta * (self.mu - x) + self.sigma * np.random.randn(self.action_dim)
self.obs = x + dx
return self.obs

6
codes/DDPG/main.py
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@@ -82,9 +82,9 @@ if __name__ == "__main__":
cfg = DDPGConfig()
env = NormalizedActions(gym.make("Pendulum-v0"))
env.seed(1) # 设置env随机种子
n_states = env.observation_space.shape[0]
n_actions = env.action_space.shape[0]
agent = DDPG(n_states,n_actions,cfg)
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]
agent = DDPG(state_dim,action_dim,cfg)
rewards,ma_rewards = train(cfg,env,agent)
agent.save(path=SAVED_MODEL_PATH)
save_results(rewards,ma_rewards,tag='train',path=RESULT_PATH)