mirror of
https://github.com/wassname/DeepRL.git
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93 lines
4.0 KiB
Python
93 lines
4.0 KiB
Python
#######################################################################
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# Copyright (C) 2017 Shangtong Zhang(zhangshangtong.cpp@gmail.com) #
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# Permission given to modify the code as long as you keep this #
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# declaration at the top #
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#######################################################################
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import numpy as np
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import torch.multiprocessing as mp
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from network import *
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from utils import *
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from component import *
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from async_worker import *
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import pickle
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import os
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import time
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class AsyncAgent:
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def __init__(self, config):
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self.config = config
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learning_network = config.network_fn()
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learning_network.share_memory()
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target_network = config.network_fn()
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target_network.share_memory()
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target_network.load_state_dict(learning_network.state_dict())
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self.task = config.task_fn()
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self.config.learning_network = learning_network
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self.config.target_network = target_network
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self.config.steps_lock = mp.Lock()
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self.config.network_lock = mp.Lock()
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self.config.total_steps = mp.Value('i', 0)
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self.config.stop_signal = mp.Value('i', False)
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def train(self, id):
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worker = self.config.worker(self.config)
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episode = 0
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rewards = []
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while not self.config.stop_signal.value:
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steps, reward = worker.episode()
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rewards.append(reward)
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if len(rewards) > 100: rewards.pop(0)
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self.config.logger.debug('worker %d, episode %d, return %f, avg return %f, episode steps %d, total steps %d' % (
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id, episode, rewards[-1], np.mean(rewards[-100:]), steps, self.config.total_steps.value))
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def save(self, file_name):
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with open(file_name, 'wb') as f:
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pickle.dump(self.config.learning_network.state_dict(), f)
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def evaluate(self, id):
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test_rewards = []
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test_points = []
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worker = self.config.worker(self.config)
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while True:
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steps = self.config.total_steps.value
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if steps % self.config.test_interval == 0:
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worker.worker_network.load_state_dict(self.config.learning_network.state_dict())
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self.save('data/%s-%s-model-%s.bin' % (
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self.config.tag, self.config.worker.__name__, self.task.name))
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rewards = np.zeros(self.config.test_repetitions)
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for i in range(self.config.test_repetitions):
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rewards[i] = worker.episode(deterministic=True)[1]
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self.config.logger.info('total steps: %d, averaged return per episode: %f(%f)' %\
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(steps, np.mean(rewards), np.std(rewards) / np.sqrt(self.config.test_repetitions)))
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test_rewards.append(np.mean(rewards))
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test_points.append(steps)
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with open('data/%s-%s-statistics-%s.bin' % (
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self.config.tag, self.config.worker.__name__, self.task.name
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), 'wb') as f:
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pickle.dump([test_points, test_rewards], f)
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if np.mean(rewards) > self.task.success_threshold:
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self.config.stop_signal.value = True
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break
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def run(self):
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os.environ['OMP_NUM_THREADS'] = '1'
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procs = [mp.Process(target=self.train, args=(i, )) for i in range(self.config.num_workers)]
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procs.append(mp.Process(target=self.evaluate, args=(self.config.num_workers, )))
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for p in procs: p.start()
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while True:
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time.sleep(1)
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for i, p in enumerate(procs):
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if not p.is_alive() and not self.config.stop_signal.value:
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self.config.logger.warning('Worker %d exited unexpectedly.' % i)
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p.terminate()
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procs[i] = mp.Process(target=self.train, args=(i, ))
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procs[i].start()
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self.config.logger.warning('Worker %d restarted.' % i)
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break
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if self.config.stop_signal.value:
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break
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for p in procs: p.join()
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