mirror of
https://github.com/vale981/ray
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134 lines
5.2 KiB
Python
134 lines
5.2 KiB
Python
import logging
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import numpy as np
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from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
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from ray.rllib.models.torch.misc import SlimFC, AppendBiasLayer, \
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normc_initializer
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from ray.rllib.utils.annotations import override
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from ray.rllib.utils.framework import get_activation_fn
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from ray.rllib.utils import try_import_torch
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torch, nn = try_import_torch()
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logger = logging.getLogger(__name__)
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class FullyConnectedNetwork(TorchModelV2, nn.Module):
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"""Generic fully connected network."""
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def __init__(self, obs_space, action_space, num_outputs, model_config,
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name):
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TorchModelV2.__init__(self, obs_space, action_space, num_outputs,
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model_config, name)
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nn.Module.__init__(self)
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activation = get_activation_fn(
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model_config.get("fcnet_activation"), framework="torch")
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hiddens = model_config.get("fcnet_hiddens")
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no_final_linear = model_config.get("no_final_linear")
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self.vf_share_layers = model_config.get("vf_share_layers")
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self.free_log_std = model_config.get("free_log_std")
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# Generate free-floating bias variables for the second half of
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# the outputs.
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if self.free_log_std:
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assert num_outputs % 2 == 0, (
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"num_outputs must be divisible by two", num_outputs)
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num_outputs = num_outputs // 2
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layers = []
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prev_layer_size = int(np.product(obs_space.shape))
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self._logits = None
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# Create layers 0 to second-last.
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for size in hiddens[:-1]:
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layers.append(
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SlimFC(
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in_size=prev_layer_size,
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out_size=size,
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initializer=normc_initializer(1.0),
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activation_fn=activation))
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prev_layer_size = size
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# The last layer is adjusted to be of size num_outputs, but it's a
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# layer with activation.
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if no_final_linear and num_outputs:
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layers.append(
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SlimFC(
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in_size=prev_layer_size,
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out_size=num_outputs,
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initializer=normc_initializer(1.0),
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activation_fn=activation))
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prev_layer_size = num_outputs
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# Finish the layers with the provided sizes (`hiddens`), plus -
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# iff num_outputs > 0 - a last linear layer of size num_outputs.
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else:
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if len(hiddens) > 0:
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layers.append(
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SlimFC(
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in_size=prev_layer_size,
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out_size=hiddens[-1],
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initializer=normc_initializer(1.0),
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activation_fn=activation))
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prev_layer_size = hiddens[-1]
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if num_outputs:
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self._logits = SlimFC(
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in_size=prev_layer_size,
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out_size=num_outputs,
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initializer=normc_initializer(0.01),
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activation_fn=None)
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else:
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self.num_outputs = (
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[np.product(obs_space.shape)] + hiddens[-1:])[-1]
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# Layer to add the log std vars to the state-dependent means.
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if self.free_log_std and self._logits:
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self._append_free_log_std = AppendBiasLayer(num_outputs)
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self._hidden_layers = nn.Sequential(*layers)
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self._value_branch_separate = None
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if not self.vf_share_layers:
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# Build a parallel set of hidden layers for the value net.
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prev_vf_layer_size = int(np.product(obs_space.shape))
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self._value_branch_separate = []
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for size in hiddens:
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self._value_branch_separate.append(
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SlimFC(
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in_size=prev_vf_layer_size,
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out_size=size,
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activation_fn=activation,
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initializer=normc_initializer(1.0)))
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prev_vf_layer_size = size
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self._value_branch_separate = nn.Sequential(
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*self._value_branch_separate)
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self._value_branch = SlimFC(
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in_size=prev_layer_size,
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out_size=1,
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initializer=normc_initializer(1.0),
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activation_fn=None)
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# Holds the current "base" output (before logits layer).
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self._features = None
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# Holds the last input, in case value branch is separate.
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self._last_flat_in = None
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@override(TorchModelV2)
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def forward(self, input_dict, state, seq_lens):
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obs = input_dict["obs_flat"].float()
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self._last_flat_in = obs.reshape(obs.shape[0], -1)
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self._features = self._hidden_layers(self._last_flat_in)
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logits = self._logits(self._features) if self._logits else \
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self._features
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if self.free_log_std:
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logits = self._append_free_log_std(logits)
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return logits, state
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@override(TorchModelV2)
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def value_function(self):
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assert self._features is not None, "must call forward() first"
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if self._value_branch_separate:
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return self._value_branch(
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self._value_branch_separate(self._last_flat_in)).squeeze(1)
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else:
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return self._value_branch(self._features).squeeze(1)
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