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https://github.com/vale981/ray
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428516056a
* Policy-classes cleanup and torch/tf unification. - Make Policy abstract. - Add `action_dist` to call to `extra_action_out_fn` (necessary for PPO torch). - Move some methods and vars to base Policy (from TFPolicy): num_state_tensors, ACTION_PROB, ACTION_LOGP and some more. * Fix `clip_action` import from Policy (should probably be moved into utils altogether). * - Move `is_recurrent()` and `num_state_tensors()` into TFPolicy (from DynamicTFPolicy). - Add config to all Policy c'tor calls (as 3rd arg after obs and action spaces). * Add `config` to c'tor call to TFPolicy. * Add missing `config` to c'tor call to TFPolicy in marvil_policy.py. * Fix test_rollout_worker.py::MockPolicy and BadPolicy classes (Policy base class is now abstract). * Fix LINT errors in Policy classes. * Implement StatefulPolicy abstract methods in test cases: test_multi_agent_env.py. * policy.py LINT errors. * Create a simple TestPolicy to sub-class from when testing Policies (reduces code in some test cases). * policy.py - Remove abstractmethod from `apply_gradients` and `compute_gradients` (these are not required iff `learn_on_batch` implemented). - Fix docstring of `num_state_tensors`. * Make QMIX torch Policy a child of TorchPolicy (instead of Policy). * QMixPolicy add empty implementations of abstract Policy methods. * Store Policy's config in self.config in base Policy c'tor. * - Make only compute_actions in base Policy's an abstractmethod and provide pass implementation to all other methods if not defined. - Fix state_batches=None (most Policies don't have internal states). * Cartpole tf learning. * Cartpole tf AND torch learning (in ~ same ts). * Cartpole tf AND torch learning (in ~ same ts). 2 * Cartpole tf (torch syntax-broken) learning (in ~ same ts). 3 * Cartpole tf AND torch learning (in ~ same ts). 4 * Cartpole tf AND torch learning (in ~ same ts). 5 * Cartpole tf AND torch learning (in ~ same ts). 6 * Cartpole tf AND torch learning (in ~ same ts). Pendulum tf learning. * WIP. * WIP. * SAC torch learning Pendulum. * WIP. * SAC torch and tf learning Pendulum and Cartpole after cleanup. * WIP. * LINT. * LINT. * SAC: Move policy.target_model to policy.device as well. * Fixes and cleanup. * Fix data-format of tf keras Conv2d layers (broken for some tf-versions which have data_format="channels_first" as default). * Fixes and LINT. * Fixes and LINT. * Fix and LINT. * WIP. * Test fixes and LINT. * Fixes and LINT. Co-authored-by: Sven Mika <sven@Svens-MacBook-Pro.local>
56 lines
1.9 KiB
Python
56 lines
1.9 KiB
Python
from ray.rllib.models.model import Model
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from ray.rllib.models.tf.misc import 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, try_import_tf
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tf = try_import_tf()
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# Deprecated: see as an alternative models/tf/fcnet_v2.py
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class FullyConnectedNetwork(Model):
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"""Generic fully connected network."""
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@override(Model)
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def _build_layers(self, inputs, num_outputs, options):
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"""Process the flattened inputs.
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Note that dict inputs will be flattened into a vector. To define a
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model that processes the components separately, use _build_layers_v2().
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"""
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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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if len(inputs.shape) > 2:
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inputs = tf.layers.flatten(inputs)
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with tf.name_scope("fc_net"):
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i = 1
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last_layer = inputs
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for size in hiddens:
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# skip final linear layer
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if options.get("no_final_linear") and i == len(hiddens):
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output = tf.layers.dense(
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last_layer,
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num_outputs,
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kernel_initializer=normc_initializer(1.0),
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activation=activation,
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name="fc_out")
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return output, output
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label = "fc{}".format(i)
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last_layer = tf.layers.dense(
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last_layer,
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size,
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kernel_initializer=normc_initializer(1.0),
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activation=activation,
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name=label)
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i += 1
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output = tf.layers.dense(
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last_layer,
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num_outputs,
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kernel_initializer=normc_initializer(0.01),
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activation=None,
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name="fc_out")
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return output, last_layer
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