具有离散动作空间的软演员评论家

Question

我正在尝试为离散动作空间实现软演员评论家算法，但我在损失函数方面遇到了麻烦。

这是来自 SAC 的具有连续动作空间的链接： https : //spinningup.openai.com/en/latest/algorithms/sac.html

我不知道我做错了什么。

问题是网络在cartpole环境中没有学到任何东西。

github上的完整代码： https : //github.com/tk2232/sac_discrete/blob/master/sac_discrete.py

这是我的想法，如何计算离散动作的损失。

价值网络

class ValueNet:
    def __init__(self, sess, state_size, hidden_dim, name):
        self.sess = sess

        with tf.variable_scope(name):
            self.states = tf.placeholder(dtype=tf.float32, shape=[None, state_size], name='value_states')
            self.targets = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='value_targets')
            x = Dense(units=hidden_dim, activation='relu')(self.states)
            x = Dense(units=hidden_dim, activation='relu')(x)
            self.values = Dense(units=1, activation=None)(x)

            optimizer = tf.train.AdamOptimizer(0.001)

            loss = 0.5 * tf.reduce_mean((self.values - tf.stop_gradient(self.targets)) ** 2)
            self.train_op = optimizer.minimize(loss, var_list=_params(name))

    def get_value(self, s):
        return self.sess.run(self.values, feed_dict={self.states: s})

    def update(self, s, targets):
        self.sess.run(self.train_op, feed_dict={self.states: s, self.targets: targets})

在 Q_Network 中，我使用收集的动作收集值

例子

q_out = [[0.5533, 0.4444], [0.2222, 0.6666]]
collected_actions = [0, 1]
gather = [[0.5533], [0.6666]]

收集功能

def gather_tensor(params, idx):
    idx = tf.stack([tf.range(tf.shape(idx)[0]), idx[:, 0]], axis=-1)
    params = tf.gather_nd(params, idx)
    return params

Q网

class SoftQNetwork:
    def __init__(self, sess, state_size, action_size, hidden_dim, name):
        self.sess = sess

        with tf.variable_scope(name):
            self.states = tf.placeholder(dtype=tf.float32, shape=[None, state_size], name='q_states')
            self.targets = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='q_targets')
            self.actions = tf.placeholder(dtype=tf.int32, shape=[None, 1], name='q_actions')

            x = Dense(units=hidden_dim, activation='relu')(self.states)
            x = Dense(units=hidden_dim, activation='relu')(x)
            x = Dense(units=action_size, activation=None)(x)
            self.q = tf.reshape(gather_tensor(x, self.actions), shape=(-1, 1))

            optimizer = tf.train.AdamOptimizer(0.001)

            loss = 0.5 * tf.reduce_mean((self.q - tf.stop_gradient(self.targets)) ** 2)
            self.train_op = optimizer.minimize(loss, var_list=_params(name))

    def update(self, s, a, target):
        self.sess.run(self.train_op, feed_dict={self.states: s, self.actions: a, self.targets: target})

    def get_q(self, s, a):
        return self.sess.run(self.q, feed_dict={self.states: s, self.actions: a})

政策网

class PolicyNet:
    def __init__(self, sess, state_size, action_size, hidden_dim):
        self.sess = sess

        with tf.variable_scope('policy_net'):
            self.states = tf.placeholder(dtype=tf.float32, shape=[None, state_size], name='policy_states')
            self.targets = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='policy_targets')
            self.actions = tf.placeholder(dtype=tf.int32, shape=[None, 1], name='policy_actions')

            x = Dense(units=hidden_dim, activation='relu')(self.states)
            x = Dense(units=hidden_dim, activation='relu')(x)
            self.logits = Dense(units=action_size, activation=None)(x)
            dist = Categorical(logits=self.logits)

            optimizer = tf.train.AdamOptimizer(0.001)

            # Get action
            self.new_action = dist.sample()
            self.new_log_prob = dist.log_prob(self.new_action)

            # Calc loss
            log_prob = dist.log_prob(tf.squeeze(self.actions))
            loss = tf.reduce_mean(tf.squeeze(self.targets) - 0.2 * log_prob)
            self.train_op = optimizer.minimize(loss, var_list=_params('policy_net'))

    def get_action(self, s):
        action = self.sess.run(self.new_action, feed_dict={self.states: s[np.newaxis, :]})
        return action[0]

    def get_next_action(self, s):
        next_action, next_log_prob = self.sess.run([self.new_action, self.new_log_prob], feed_dict={self.states: s})
        return next_action.reshape((-1, 1)), next_log_prob.reshape((-1, 1))

    def update(self, s, a, target):
        self.sess.run(self.train_op, feed_dict={self.states: s, self.actions: a, self.targets: target})

更新功能

def soft_q_update(batch_size, frame_idx):
    gamma = 0.99
    alpha = 0.2

    state, action, reward, next_state, done = replay_buffer.sample(batch_size)
    action = action.reshape((-1, 1))
    reward = reward.reshape((-1, 1))
    done = done.reshape((-1, 1))

Q_target

v_ = value_net_target.get_value(next_state)
q_target = reward + (1 - done) * gamma * v_

V_target

next_action, next_log_prob = policy_net.get_next_action(state)
q1 = soft_q_net_1.get_q(state, next_action)
q2 = soft_q_net_2.get_q(state, next_action)
q = np.minimum(q1, q2)
v_target = q - alpha * next_log_prob

策略目标

q1 = soft_q_net_1.get_q(state, action)
q2 = soft_q_net_2.get_q(state, action)
policy_target = np.minimum(q1, q2)

Answer 1

由于该算法适用于离散和连续策略，关键思想是我们需要一个可重新参数化的离散分布。 然后扩展应该涉及来自连续 SAC 的最小代码修改——只需更改策略分发类。

有一种这样的分布——GumbelSoftmax 分布。 PyTorch 没有这个内置函数，所以我只是从具有正确 rsample() 的近亲扩展它并添加正确的 log prob 计算方法。 由于能够计算重新参数化的动作及其对数概率，SAC 可以用最少的额外代码完美地处理离散动作，如下所示。

    def calc_log_prob_action(self, action_pd, reparam=False):
        '''Calculate log_probs and actions with option to reparametrize from paper eq. 11'''
        samples = action_pd.rsample() if reparam else action_pd.sample()
        if self.body.is_discrete:  # this is straightforward using GumbelSoftmax
            actions = samples
            log_probs = action_pd.log_prob(actions)
        else:
            mus = samples
            actions = self.scale_action(torch.tanh(mus))
            # paper Appendix C. Enforcing Action Bounds for continuous actions
            log_probs = (action_pd.log_prob(mus) - torch.log(1 - actions.pow(2) + 1e-6).sum(1))
        return log_probs, actions


# ... for discrete action, GumbelSoftmax distribution

class GumbelSoftmax(distributions.RelaxedOneHotCategorical):
    '''
    A differentiable Categorical distribution using reparametrization trick with Gumbel-Softmax
    Explanation http://amid.fish/assets/gumbel.html
    NOTE: use this in place PyTorch's RelaxedOneHotCategorical distribution since its log_prob is not working right (returns positive values)
    Papers:
    [1] The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables (Maddison et al, 2017)
    [2] Categorical Reparametrization with Gumbel-Softmax (Jang et al, 2017)
    '''

    def sample(self, sample_shape=torch.Size()):
        '''Gumbel-softmax sampling. Note rsample is inherited from RelaxedOneHotCategorical'''
        u = torch.empty(self.logits.size(), device=self.logits.device, dtype=self.logits.dtype).uniform_(0, 1)
        noisy_logits = self.logits - torch.log(-torch.log(u))
        return torch.argmax(noisy_logits, dim=-1)

    def log_prob(self, value):
        '''value is one-hot or relaxed'''
        if value.shape != self.logits.shape:
            value = F.one_hot(value.long(), self.logits.shape[-1]).float()
            assert value.shape == self.logits.shape
        return - torch.sum(- value * F.log_softmax(self.logits, -1), -1)

这是 LunarLander 的结果。 SAC 学会了非常快地解决它。

完整的实现代码在SLM 实验室https://github.com/kengz/SLM-Lab/blob/master/slm_lab/agent/algorithm/sac.py

Roboschool（连续）和 LunarLander（离散）的 SAC 基准测试结果如下所示： https : //github.com/kengz/SLM-Lab/pull/399

Answer 2

可能这个repo可能会有所帮助。 描述说，该存储库包含 PyTorch 上离散动作空间的 SAC 实现。 有文件与SAC算法连续动作空间和文件与SAC适于离散动作空间。

Answer 3

有一篇关于具有离散动作空间的 SAC 的论文。 它说离散动作空间的 SAC 不需要像 Gumbel softmax 这样的重新参数化技巧。 相反，SAC 需要一些修改。 请参阅论文了解更多详情。

论文/ 作者的实现（无 atari 代码） /复制（有 atari 代码）

我希望它能帮助你。

Answer 4

Pytorch 1.8 有RelaxedOneHotCategorical，这支持使用gumbel softmax 重新参数化采样。

import torch
import torch.nn as nn
from torch.distributions import RelaxedOneHotCategorical

    class Policy(nn.Module):
        def __init__(self, input_dims, hidden_dims, actions):
            super().__init__()
            self.mlp = nn.Sequential(nn.Linear(input_dims, hidden_dims), nn.SELU(inplace=True),
                                        nn.Linear(hidden_dims, hidden_dims), nn.SELU(inplace=True),
                                        nn.Linear(hidden_dims, out_dims))

        def forward(self, state):
            logits = torch.log_softmax(self.mlp(state), dim=-1)
            return RelaxedOneHotCategorical(logits=logits, temperature=torch.ones(1) * 1.0)

>>> policy = Policy(4, 16, 2)
>>> a_dist = policy(torch.randn(8, 4))
>>> a_dist.rsample()
tensor([[0.0353, 0.9647],
        [0.1348, 0.8652],
        [0.1110, 0.8890],
        [0.4956, 0.5044],
        [0.6941, 0.3059],
        [0.6126, 0.3874],
        [0.2932, 0.7068],
        [0.0498, 0.9502]], grad_fn=<ExpBackward>)