mirror of
https://github.com/wassname/ray.git
synced 2026-06-30 00:49:16 +08:00
Eric Liang
58 lines
2.0 KiB
Python
58 lines
2.0 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import logging
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import numpy as np
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import torch.nn as nn
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from ray.rllib.models.torch.model import TorchModel
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from ray.rllib.models.torch.misc import normc_initializer, SlimFC, \
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_get_activation_fn
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from ray.rllib.utils.annotations import override
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logger = logging.getLogger(__name__)
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class FullyConnectedNetwork(TorchModel):
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"""Generic fully connected network."""
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def __init__(self, obs_space, num_outputs, options):
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TorchModel.__init__(self, obs_space, num_outputs, options)
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hiddens = options.get("fcnet_hiddens")
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activation = _get_activation_fn(options.get("fcnet_activation"))
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logger.debug("Constructing fcnet {} {}".format(hiddens, activation))
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layers = []
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last_layer_size = np.product(obs_space.shape)
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for size in hiddens:
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layers.append(
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SlimFC(
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in_size=last_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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last_layer_size = size
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self._hidden_layers = nn.Sequential(*layers)
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self._logits = SlimFC(
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in_size=last_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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self._value_branch = SlimFC(
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in_size=last_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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@override(nn.Module)
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def forward(self, input_dict, hidden_state):
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# Note that we override forward() and not _forward() to get the
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# flattened obs here.
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obs = input_dict["obs"]
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features = self._hidden_layers(obs.reshape(obs.shape[0], -1))
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logits = self._logits(features)
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value = self._value_branch(features).squeeze(1)
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return logits, features, value, hidden_state
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