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https://github.com/vale981/ray
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60d4d5e1aa
* Remove all __future__ imports from RLlib. * Remove (object) again from tf_run_builder.py::TFRunBuilder. * Fix 2xLINT warnings. * Fix broken appo_policy import (must be appo_tf_policy) * Remove future imports from all other ray files (not just RLlib). * Remove future imports from all other ray files (not just RLlib). * Remove future import blocks that contain `unicode_literals` as well. Revert appo_tf_policy.py to appo_policy.py (belongs to another PR). * Add two empty lines before Schedule class. * Put back __future__ imports into determine_tests_to_run.py. Fails otherwise on a py2/print related error.
63 lines
2.2 KiB
Python
63 lines
2.2 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 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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from ray.rllib.utils import try_import_torch
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_, 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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hiddens = model_config.get("fcnet_hiddens")
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activation = _get_activation_fn(model_config.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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self._cur_value = 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"]
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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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self._cur_value = self._value_branch(features).squeeze(1)
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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._cur_value is not None, "must call forward() first"
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return self._cur_value
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