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Support shared stats for continuous A3C

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Shangtong Zhang
2017-10-04 20:58:42 -06:00
parent 3935dfc428
commit c773da7d08
7 changed files with 105 additions and 53 deletions
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agent/async_agent.py
+12 -4
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@@ -25,10 +25,10 @@ def train(id, config, learning_network, target_network):
config.logger.debug('worker %d, episode %d, return %f, avg return %f, episode steps %d, total steps %d' % (
id, episode, rewards[-1], np.mean(rewards[-100:]), steps, config.total_steps.value))
def evaluate(config, task, learning_network):
def evaluate(config, task, learning_network, extra):
test_rewards = []
test_points = []
worker = config.worker(config, learning_network, None)
worker = config.worker(config, learning_network, extra)
# config.logger = Logger('./evaluation_log', gym.logger)
while True:
steps = config.total_steps.value
@@ -69,8 +69,16 @@ class AsyncAgent:
target_network.load_state_dict(learning_network.state_dict())
os.environ['OMP_NUM_THREADS'] = '1'
args = [(i, config, learning_network, target_network) for i in range(config.num_workers)]
args.append((config, task, learning_network))
if config.worker == NStepQLearning or config.worker == OneStepQLearning or config.worker == OneStepSarsa:
extra = target_network
elif config.worker == ContinuousAdvantageActorCritic:
state_normalizer = StaticNormalizer(task.state_dim)
reward_normalizer = StaticNormalizer(1)
extra = [state_normalizer, reward_normalizer]
else:
extra = None
args = [(i, config, learning_network, extra) for i in range(config.num_workers)]
args.append((config, task, learning_network, extra))
procs = [mp.Process(target=train, args=args[i]) for i in range(config.num_workers)]
procs.append(mp.Process(target=evaluate, args=args[-1]))
for p in procs: p.start()