mirror of
https://github.com/vale981/ray
synced 2025-03-10 13:26:39 -04:00
555dcf35a2
* add policy gradient example * fix typos * Minor changes plus some documentation. * Minor fixes.
41 lines
1.5 KiB
Python
41 lines
1.5 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 os
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import ray
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from reinforce.env import BatchedEnv
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from reinforce.policy import ProximalPolicyLoss
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from reinforce.filter import MeanStdFilter
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from reinforce.rollout import rollouts, add_advantage_values
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class Agent(object):
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def __init__(self, name, batchsize, config, use_gpu):
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if not use_gpu:
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os.environ["CUDA_VISIBLE_DEVICES"] = ""
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self.env = BatchedEnv(name, batchsize, preprocessor=None)
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self.sess = tf.Session()
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self.ppo = ProximalPolicyLoss(self.env.observation_space, self.env.action_space, config, self.sess)
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self.optimizer = tf.train.AdamOptimizer(config["sgd_stepsize"])
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self.train_op = self.optimizer.minimize(self.ppo.loss)
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self.variables = ray.experimental.TensorFlowVariables(self.ppo.loss, self.sess)
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self.observation_filter = MeanStdFilter(self.env.observation_space.shape, clip=None)
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self.reward_filter = MeanStdFilter((), clip=5.0)
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self.sess.run(tf.global_variables_initializer())
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def get_weights(self):
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return self.variables.get_weights()
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def load_weights(self, weights):
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self.variables.set_weights(weights)
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def compute_trajectory(self, gamma, lam, horizon):
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trajectory = rollouts(self.ppo, self.env, horizon, self.observation_filter, self.reward_filter)
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add_advantage_values(trajectory, gamma, lam, self.reward_filter)
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return trajectory
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RemoteAgent = ray.actor(Agent)
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