Source code for openrl.modules.networks.policy_value_network

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 2021 The OpenRL Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""

import torch
import torch.nn as nn

from openrl.buffers.utils.util import get_policy_obs_space
from openrl.modules.networks.base_value_policy_network import BaseValuePolicyNetwork
from openrl.modules.networks.utils.act import ACTLayer
from openrl.modules.networks.utils.cnn import CNNBase
from openrl.modules.networks.utils.mlp import MLPBase, MLPLayer
from openrl.modules.networks.utils.popart import PopArt
from openrl.modules.networks.utils.rnn import RNNLayer
from openrl.modules.networks.utils.util import init
from openrl.utils.util import check_v2 as check


class PolicyValueNetwork(BaseValuePolicyNetwork):
    def __init__(
        self,
        cfg,
        input_space,
        action_space,
        device=torch.device("cpu"),
        use_half=False,
        extra_args=None,
    ):
        super(PolicyValueNetwork, self).__init__(cfg, device)
        self._gain = cfg.gain
        self._use_orthogonal = cfg.use_orthogonal
        self._activation_id = cfg.activation_id
        self._recurrent_N = cfg.recurrent_N
        self._use_naive_recurrent_policy = cfg.use_naive_recurrent_policy
        self._use_recurrent_policy = cfg.use_recurrent_policy

        self._use_popart = cfg.use_popart
        self.hidden_size = cfg.hidden_size
        self.device = device
        self.use_half = use_half
        self.tpdv = dict(dtype=torch.float32, device=device)
        init_method = [nn.init.xavier_uniform_, nn.init.orthogonal_][
            self._use_orthogonal
        ]

        # obs space
        policy_obs_shape = get_policy_obs_space(input_space)

        self.obs_prep = (
            CNNBase(cfg, policy_obs_shape)
            if len(policy_obs_shape) == 3
            else MLPBase(
                cfg,
                policy_obs_shape,
                use_attn_internal=cfg.use_attn_internal,
                use_cat_self=True,
            )
        )

        self.critic_obs_prep = self.obs_prep

        # common layer
        self.common = MLPLayer(
            self.hidden_size,
            self.hidden_size,
            layer_N=0,
            use_orthogonal=self._use_orthogonal,
            activation_id=self._activation_id,
        )

        if self._use_naive_recurrent_policy or self._use_recurrent_policy:
            self.rnn = RNNLayer(
                self.hidden_size,
                self.hidden_size,
                self._recurrent_N,
                self._use_orthogonal,
            )

        def init_(m):
            return init(m, init_method, lambda x: nn.init.constant_(x, 0))

        input_size = self.hidden_size

        # value
        if self._use_popart:
            self.v_out = init_(PopArt(input_size, 1, device=device))
        else:
            self.v_out = init_(nn.Linear(input_size, 1))

        # action
        self.act = ACTLayer(
            action_space, self.hidden_size, self._use_orthogonal, self._gain
        )
        if use_half:
            self.half()
        self.to(self.device)

    def get_actions(
        self, obs, rnn_states, masks, action_masks=None, deterministic=False
    ):
        obs = check(obs, self.use_half, self.tpdv)
        rnn_states = check(rnn_states, self.use_half, self.tpdv)
        masks = check(masks, self.use_half, self.tpdv)
        if action_masks is not None:
            action_masks = check(action_masks, self.use_half, self.tpdv)

        x = obs
        x = self.obs_prep(x)

        # common
        actor_features = self.common(x)
        if self._use_naive_recurrent_policy or self._use_recurrent_policy:
            actor_features, rnn_states = self.rnn(actor_features, rnn_states, masks)

        actions, action_log_probs = self.act(
            actor_features, action_masks, deterministic
        )

        return actions, action_log_probs, rnn_states

    def eval_actions(
        self, obs, rnn_states, action, masks, action_masks, active_masks=None
    ):
        obs = check(obs, self.use_half, self.tpdv)
        rnn_states = check(rnn_states, self.use_half, self.tpdv)
        action = check(action, self.use_half, self.tpdv)
        masks = check(masks, self.use_half, self.tpdv)
        if action_masks is not None:
            action_masks = check(action_masks, self.use_half, self.tpdv)
        if active_masks is not None:
            active_masks = check(active_masks, self.use_half, self.tpdv)

        x = obs
        x = self.obs_prep(x)

        actor_features = self.common(x)
        if self._use_naive_recurrent_policy or self._use_recurrent_policy:
            actor_features, rnn_states = self.rnn(actor_features, rnn_states, masks)

        action_log_probs, dist_entropy = self.act.evaluate_actions(
            actor_features, action, action_masks, active_masks
        )

        return action_log_probs, dist_entropy, None

    def get_values(self, critic_obs, rnn_states, masks):
        critic_obs = check(critic_obs, self.use_half, self.tpdv)
        rnn_states = check(rnn_states, self.use_half, self.tpdv)
        masks = check(masks, self.use_half, self.tpdv)

        share_x = critic_obs
        share_x = self.critic_obs_prep(share_x)

        critic_features = self.common(share_x)
        if self._use_naive_recurrent_policy or self._use_recurrent_policy:
            critic_features, rnn_states = self.rnn(critic_features, rnn_states, masks)

        values = self.v_out(critic_features)

        return values, rnn_states