mirror of
https://github.com/vale981/ray
synced 2025-03-06 18:41:40 -05:00
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>
93 lines
3.6 KiB
Python
93 lines
3.6 KiB
Python
import numpy as np
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from ray.rllib.models.tf.misc import normc_initializer
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from ray.rllib.models.tf.tf_modelv2 import TFModelV2
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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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class FullyConnectedNetwork(TFModelV2):
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"""Generic fully connected network implemented in ModelV2 API."""
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def __init__(self, obs_space, action_space, num_outputs, model_config,
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name):
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super(FullyConnectedNetwork, self).__init__(
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obs_space, action_space, num_outputs, model_config, name)
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activation = get_activation_fn(model_config.get("fcnet_activation"))
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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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vf_share_layers = model_config.get("vf_share_layers")
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# we are using obs_flat, so take the flattened shape as input
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inputs = tf.keras.layers.Input(
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shape=(np.product(obs_space.shape), ), name="observations")
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last_layer = layer_out = inputs
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i = 1
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# Create layers 0 to second-last.
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for size in hiddens[:-1]:
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last_layer = tf.keras.layers.Dense(
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size,
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name="fc_{}".format(i),
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activation=activation,
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kernel_initializer=normc_initializer(1.0))(last_layer)
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i += 1
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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 self.num_outputs:
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layer_out = tf.keras.layers.Dense(
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self.num_outputs,
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name="fc_out",
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activation=activation,
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kernel_initializer=normc_initializer(1.0))(last_layer)
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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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last_layer = tf.keras.layers.Dense(
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hiddens[-1],
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name="fc_{}".format(i),
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activation=activation,
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kernel_initializer=normc_initializer(1.0))(last_layer)
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if self.num_outputs:
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layer_out = tf.keras.layers.Dense(
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self.num_outputs,
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name="fc_out",
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activation=None,
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kernel_initializer=normc_initializer(0.01))(last_layer)
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# Adjust self.num_outputs to be the number of nodes in the last
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# layer.
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else:
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self.num_outputs = (
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[np.product(obs_space.shape)] + hiddens[-1:-1])[-1]
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if not vf_share_layers:
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# build a parallel set of hidden layers for the value net
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last_layer = inputs
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i = 1
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for size in hiddens:
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last_layer = tf.keras.layers.Dense(
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size,
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name="fc_value_{}".format(i),
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activation=activation,
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kernel_initializer=normc_initializer(1.0))(last_layer)
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i += 1
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value_out = tf.keras.layers.Dense(
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1,
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name="value_out",
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activation=None,
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kernel_initializer=normc_initializer(0.01))(last_layer)
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self.base_model = tf.keras.Model(inputs, [layer_out, value_out])
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self.register_variables(self.base_model.variables)
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def forward(self, input_dict, state, seq_lens):
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model_out, self._value_out = self.base_model(input_dict["obs_flat"])
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return model_out, state
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def value_function(self):
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return tf.reshape(self._value_out, [-1])
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