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https://github.com/vale981/ray
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42 lines
1.5 KiB
Python
42 lines
1.5 KiB
Python
from ray.rllib.models.modelv2 import ModelV2
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from ray.rllib.models.preprocessors import get_preprocessor
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from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
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from ray.rllib.utils.annotations import override
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from ray.rllib.utils.framework import try_import_torch
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torch, nn = try_import_torch()
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class RNNModel(TorchModelV2, nn.Module):
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"""The default RNN model for QMIX."""
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def __init__(self, obs_space, action_space, num_outputs, model_config, name):
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TorchModelV2.__init__(
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self, obs_space, action_space, num_outputs, model_config, name
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)
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nn.Module.__init__(self)
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self.obs_size = _get_size(obs_space)
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self.rnn_hidden_dim = model_config["lstm_cell_size"]
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self.fc1 = nn.Linear(self.obs_size, self.rnn_hidden_dim)
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self.rnn = nn.GRUCell(self.rnn_hidden_dim, self.rnn_hidden_dim)
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self.fc2 = nn.Linear(self.rnn_hidden_dim, num_outputs)
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self.n_agents = model_config["n_agents"]
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@override(ModelV2)
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def get_initial_state(self):
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# Place hidden states on same device as model.
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return [
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self.fc1.weight.new(self.n_agents, self.rnn_hidden_dim).zero_().squeeze(0)
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]
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@override(ModelV2)
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def forward(self, input_dict, hidden_state, seq_lens):
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x = nn.functional.relu(self.fc1(input_dict["obs_flat"].float()))
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h_in = hidden_state[0].reshape(-1, self.rnn_hidden_dim)
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h = self.rnn(x, h_in)
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q = self.fc2(h)
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return q, [h]
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def _get_size(obs_space):
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return get_preprocessor(obs_space)(obs_space).size
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