mirror of
https://github.com/vale981/ray
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42991d723f
Cleans up of the rllib/examples folder by moving all example Envs into rllibexamples/env (so they can be used by other scripts and tests as well).
117 lines
4.1 KiB
Python
117 lines
4.1 KiB
Python
"""Example of using a custom RNN keras model."""
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import argparse
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import numpy as np
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import ray
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from ray import tune
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from ray.tune.registry import register_env
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from ray.rllib.examples.env.repeat_after_me_env import RepeatAfterMeEnv
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from ray.rllib.examples.env.repeat_initial_obs_env import RepeatInitialObsEnv
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from ray.rllib.models import ModelCatalog
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from ray.rllib.models.modelv2 import ModelV2
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from ray.rllib.models.tf.recurrent_tf_modelv2 import RecurrentTFModelV2
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from ray.rllib.utils.annotations import override
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from ray.rllib.utils import try_import_tf
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tf = try_import_tf()
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parser = argparse.ArgumentParser()
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parser.add_argument("--run", type=str, default="PPO")
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parser.add_argument("--env", type=str, default="RepeatAfterMeEnv")
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parser.add_argument("--stop", type=int, default=90)
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parser.add_argument("--num-cpus", type=int, default=0)
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class MyKerasRNN(RecurrentTFModelV2):
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"""Example of using the Keras functional API to define a RNN model."""
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def __init__(self,
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obs_space,
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action_space,
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num_outputs,
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model_config,
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name,
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hiddens_size=256,
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cell_size=64):
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super(MyKerasRNN, self).__init__(obs_space, action_space, num_outputs,
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model_config, name)
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self.cell_size = cell_size
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# Define input layers
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input_layer = tf.keras.layers.Input(
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shape=(None, obs_space.shape[0]), name="inputs")
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state_in_h = tf.keras.layers.Input(shape=(cell_size, ), name="h")
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state_in_c = tf.keras.layers.Input(shape=(cell_size, ), name="c")
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seq_in = tf.keras.layers.Input(shape=(), name="seq_in", dtype=tf.int32)
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# Preprocess observation with a hidden layer and send to LSTM cell
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dense1 = tf.keras.layers.Dense(
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hiddens_size, activation=tf.nn.relu, name="dense1")(input_layer)
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lstm_out, state_h, state_c = tf.keras.layers.LSTM(
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cell_size, return_sequences=True, return_state=True, name="lstm")(
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inputs=dense1,
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mask=tf.sequence_mask(seq_in),
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initial_state=[state_in_h, state_in_c])
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# Postprocess LSTM output with another hidden layer and compute values
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logits = tf.keras.layers.Dense(
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self.num_outputs,
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activation=tf.keras.activations.linear,
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name="logits")(lstm_out)
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values = tf.keras.layers.Dense(
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1, activation=None, name="values")(lstm_out)
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# Create the RNN model
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self.rnn_model = tf.keras.Model(
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inputs=[input_layer, seq_in, state_in_h, state_in_c],
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outputs=[logits, values, state_h, state_c])
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self.register_variables(self.rnn_model.variables)
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self.rnn_model.summary()
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@override(RecurrentTFModelV2)
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def forward_rnn(self, inputs, state, seq_lens):
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model_out, self._value_out, h, c = self.rnn_model([inputs, seq_lens] +
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state)
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return model_out, [h, c]
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@override(ModelV2)
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def get_initial_state(self):
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return [
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np.zeros(self.cell_size, np.float32),
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np.zeros(self.cell_size, np.float32),
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]
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@override(ModelV2)
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def value_function(self):
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return tf.reshape(self._value_out, [-1])
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if __name__ == "__main__":
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args = parser.parse_args()
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ray.init(num_cpus=args.num_cpus or None)
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ModelCatalog.register_custom_model("rnn", MyKerasRNN)
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register_env("RepeatAfterMeEnv", lambda c: RepeatAfterMeEnv(c))
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register_env("RepeatInitialObsEnv", lambda _: RepeatInitialObsEnv())
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config = {
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"env": args.env,
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"env_config": {
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"repeat_delay": 2,
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},
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"gamma": 0.9,
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"num_workers": 0,
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"num_envs_per_worker": 20,
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"entropy_coeff": 0.001,
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"num_sgd_iter": 5,
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"vf_loss_coeff": 1e-5,
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"model": {
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"custom_model": "rnn",
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"max_seq_len": 20,
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},
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}
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tune.run(
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args.run,
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config=config,
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stop={"episode_reward_mean": args.stop},
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)
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