rnn_model.py

#   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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
#
#     http://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 paddle
import paddle.nn as nn
import paddle.nn.functional as F
import parl


class RNNModel(parl.Model):
    def __init__(self, input_shape, n_actions, rnn_hidden_dim=64):
        super(RNNModel, self).__init__()
        self.rnn_hidden_dim = rnn_hidden_dim

        self.fc1 = nn.Linear(input_shape, rnn_hidden_dim)
        self.rnn = nn.GRUCell(
            input_size=rnn_hidden_dim, hidden_size=rnn_hidden_dim)
        self.fc2 = nn.Linear(rnn_hidden_dim, n_actions)

    def init_hidden(self):
        hidden_state = paddle.zeros((1, self.rnn_hidden_dim), dtype='float32')
        return hidden_state

    def forward(self, inputs, hidden_state):
        x = F.relu(self.fc1(inputs))
        h_in = hidden_state.reshape(shape=(-1, self.rnn_hidden_dim))

        _, h = self.rnn(x, h_in)
        q = self.fc2(h)  # (batch_size, n_actions)
        return q, h