本文整理汇总了Python中memory.Memory.store方法的典型用法代码示例。如果您正苦于以下问题：Python Memory.store方法的具体用法？Python Memory.store怎么用？Python Memory.store使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类memory.Memory的用法示例。
在下文中一共展示了Memory.store方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
# 需要导入模块: from memory import Memory [as 别名]
# 或者: from memory.Memory import store [as 别名]
class DDPG:
def __init__(self, obs_dim, action_dim, hiddens_actor, hiddens_critic, layer_norm=False, memory_size=50000):
self.obs_dim = obs_dim
self.action_dim = action_dim
self.noise_stddev = 1.
self.noise_stddev_decrease = 5e-4
self.noise_stddev_lower = 5e-2
actor_activations = [dy.tanh for _ in range(len(hiddens_actor))] + [dy.tanh]
critic_activations = [dy.tanh for _ in range(len(hiddens_critic))] + [None]
self.actor = MLP(inpt_shape=(obs_dim,), hiddens=hiddens_actor + [action_dim], activation=actor_activations,
layer_norm=layer_norm)
self.critic = MLP(inpt_shape=(obs_dim + action_dim,), hiddens=hiddens_critic + [1],
activation=critic_activations, layer_norm=layer_norm)
self.actor_target = MLP(inpt_shape=(obs_dim,), hiddens=hiddens_actor + [action_dim],
activation=actor_activations, layer_norm=layer_norm)
self.critic_target = MLP(inpt_shape=(obs_dim + action_dim,), hiddens=hiddens_critic + [1],
activation=critic_activations, layer_norm=layer_norm)
self.actor_target.update(self.actor, soft=False)
self.critic_target.update(self.critic, soft=False)
self.trainer_actor = dy.AdamTrainer(self.actor.pc)
self.trainer_critic = dy.AdamTrainer(self.critic.pc)
self.trainer_actor.set_learning_rate(1e-4)
self.trainer_critic.set_learning_rate(1e-3)
self.memory = Memory(memory_size)
def act(self, obs):
dy.renew_cg()
action = self.actor(obs).npvalue()
if self.noise_stddev > 0:
noise = np.random.randn(self.action_dim) * self.noise_stddev
action += noise
return np.clip(action, -1, 1)
def store(self, exp):
self.memory.store(exp)
def learn(self, batch_size):
exps = self.memory.sample(batch_size)
obss, actions, rewards, obs_nexts, dones = self._process(exps)
# Update critic
dy.renew_cg()
target_actions = self.actor_target(obs_nexts, batched=True)
target_values = self.critic_target(dy.concatenate([dy.inputTensor(obs_nexts, batched=True), target_actions]),
batched=True)
target_values = rewards + 0.99 * target_values.npvalue() * (1 - dones)
dy.renew_cg()
values = self.critic(np.concatenate([obss, actions]), batched=True)
loss = dy.mean_batches((values - dy.inputTensor(target_values, batched=True)) ** 2)
loss_value_critic = loss.npvalue()
loss.backward()
self.trainer_critic.update()
# update actor
dy.renew_cg()
actions = self.actor(obss, batched=True)
obs_and_actions = dy.concatenate([dy.inputTensor(obss, batched=True), actions])
loss = -dy.mean_batches(self.critic(obs_and_actions, batched=True))
loss_value_actor = loss.npvalue()
loss.backward()
self.trainer_actor.update()
self.noise_stddev = (
self.noise_stddev - self.noise_stddev_decrease) if self.noise_stddev > self.noise_stddev_lower else self.noise_stddev_lower
self.actor_target.update(self.actor, soft=True)
self.critic_target.update(self.critic, soft=True)
return loss_value_actor + loss_value_critic
# data in memory: [memory_size, exp], exp: [obs, action, reward, obs_next, done]
# output: [obss, actions, rewards, obs_nexts, dones], 'X's: [x, batch_size]
@staticmethod
def _process(exps):
n = len(exps)
ret = []
for i in range(5):
ret.append([])
for j in range(n):
ret[i].append(exps[j][i])
ret = [np.transpose(arr) for arr in ret]
return ret
@property
def epsilon(self):
return self.noise_stddev