mirror of
https://github.com/wassname/ray.git
synced 2026-07-05 19:05:52 +08:00
Eric Liang
107 lines
4.1 KiB
Python
107 lines
4.1 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 tensorflow as tf
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import gym
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import ray
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from ray.rllib.models.catalog import ModelCatalog
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class BCPolicy(object):
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def __init__(self, obs_space, action_space, config):
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self.local_steps = 0
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self.config = config
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self.summarize = config.get("summarize")
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self._setup_graph(obs_space, action_space)
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self.setup_loss(action_space)
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self.setup_gradients()
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self.initialize()
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def _setup_graph(self, obs_space, ac_space):
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self.x = tf.placeholder(tf.float32, [None] + list(obs_space.shape))
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dist_class, self.logit_dim = ModelCatalog.get_action_dist(
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ac_space, self.config["model"])
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self._model = ModelCatalog.get_model(self.x, self.logit_dim,
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self.config["model"])
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self.logits = self._model.outputs
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self.curr_dist = dist_class(self.logits)
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self.sample = self.curr_dist.sample()
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self.var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
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tf.get_variable_scope().name)
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def setup_loss(self, action_space):
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if isinstance(action_space, gym.spaces.Box):
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self.ac = tf.placeholder(
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tf.float32, [None] + list(action_space.shape), name="ac")
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elif isinstance(action_space, gym.spaces.Discrete):
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self.ac = tf.placeholder(tf.int64, [None], name="ac")
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else:
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raise NotImplementedError("action space" +
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str(type(action_space)) +
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"currently not supported")
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log_prob = self.curr_dist.logp(self.ac)
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self.pi_loss = -tf.reduce_sum(log_prob)
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self.loss = self.pi_loss
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def setup_gradients(self):
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grads = tf.gradients(self.loss, self.var_list)
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self.grads, _ = tf.clip_by_global_norm(grads, self.config["grad_clip"])
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grads_and_vars = list(zip(self.grads, self.var_list))
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opt = tf.train.AdamOptimizer(self.config["lr"])
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self._apply_gradients = opt.apply_gradients(grads_and_vars)
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def initialize(self):
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if self.summarize:
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bs = tf.to_float(tf.shape(self.x)[0])
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tf.summary.scalar("model/policy_loss", self.pi_loss / bs)
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tf.summary.scalar("model/grad_gnorm", tf.global_norm(self.grads))
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tf.summary.scalar("model/var_gnorm", tf.global_norm(self.var_list))
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self.summary_op = tf.summary.merge_all()
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# TODO(rliaw): Can consider exposing these parameters
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self.sess = tf.Session(
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graph=self.g,
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config=tf.ConfigProto(
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intra_op_parallelism_threads=1,
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inter_op_parallelism_threads=2,
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gpu_options=tf.GPUOptions(allow_growth=True)))
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self.variables = ray.experimental.TensorFlowVariables(
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self.loss, self.sess)
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self.sess.run(tf.global_variables_initializer())
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def compute_gradients(self, samples):
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info = {}
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feed_dict = {
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self.x: samples["observations"],
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self.ac: samples["actions"]
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}
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self.grads = [g for g in self.grads if g is not None]
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self.local_steps += 1
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if self.summarize:
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loss, grad, summ = self.sess.run(
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[self.loss, self.grads, self.summary_op], feed_dict=feed_dict)
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info["summary"] = summ
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else:
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loss, grad = self.sess.run(
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[self.loss, self.grads], feed_dict=feed_dict)
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info["num_samples"] = len(samples)
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info["loss"] = loss
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return grad, info
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def apply_gradients(self, grads):
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feed_dict = {self.grads[i]: grads[i] for i in range(len(grads))}
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self.sess.run(self._apply_gradients, feed_dict=feed_dict)
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def get_weights(self):
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weights = self.variables.get_weights()
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return weights
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def set_weights(self, weights):
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self.variables.set_weights(weights)
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def compute(self, ob, *args):
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action = self.sess.run(self.sample, {self.x: [ob]})
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return action, None
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