def prepare_experiment(env, args):
# Manager to share PER between a learner and explorers
SyncManager.register('PrioritizedReplayBuffer', PrioritizedReplayBuffer)
manager = SyncManager()
manager.start()
kwargs = get_default_rb_dict(args.replay_buffer_size, env)
kwargs["check_for_update"] = True
global_rb = manager.PrioritizedReplayBuffer(**kwargs)
# queues to share network parameters between a learner and explorers
n_queue = 1 if args.n_env > 1 else args.n_explorer
n_queue += 1 # for evaluation
queues = [manager.Queue() for _ in range(n_queue)]
# Event object to share training status. if event is set True, all exolorers stop sampling transitions
is_training_done = Event()
# Lock
lock = manager.Lock()
# Shared memory objects to count number of samples and applied gradients
trained_steps = Value('i', 0)
return global_rb, queues, is_training_done, lock, trained_steps
def main(args_):
if args_.n_explorer is None:
n_explorer = multiprocessing.cpu_count() - 1
else:
n_explorer = args_.n_explorer
assert n_explorer > 0, "[error] number of explorers must be positive integer"
env = env_fn()
# Manager to share PER between a learner and explorers
SyncManager.register('PrioritizedReplayBuffer', PrioritizedReplayBuffer)
manager = SyncManager()
manager.start()
global_rb = manager.PrioritizedReplayBuffer(
obs_shape=env.observation_space.shape,
act_dim=env.action_space.low.size,
size=args_.replay_buffer_size)
# queues to share network parameters between a learner and explorers
queues = [Queue() for _ in range(n_explorer)]
# Event object to share training status. if event is set True, all exolorers stop sampling transitions
is_training_done = Event()
# Lock
lock = manager.Lock()
# Shared memory objects to count number of samples and applied gradients
trained_steps = Value('i', 0)
n_transition = Value('i', 0)
tasks = []
# Add explorers
for i in range(n_explorer):
tasks.append(Process(
target=explorer,
args=[global_rb, queues[i], trained_steps, n_transition, is_training_done, lock,
env_fn, policy_fn, args_.local_buffer_size]))
# Add learner
tasks.append(Process(
target=learner,
args=[global_rb, trained_steps, is_training_done, lock, env_fn, policy_fn,
args_.max_batch, args_.param_update_freq, *queues]))
for task in tasks:
task.start()
for task in tasks:
task.join()
PER_a = 0.6 # P(i) = p(i) ** a / total_priority ** a
env_dict = {
"obs": {
"shape": state_size
},
"act": {},
"rew": {},
"next_obs": {
"shape": state_size
},
"done": {}
}
global_rb = manager.PrioritizedReplayBuffer(memory_size,
env_dict=env_dict,
alpha=PER_a,
default_dtype=np.float16,
check_for_update=True)
n_explorer = multiprocessing.cpu_count() - 1
epsilons = [
pow(0.4, 1 + (i / (n_explorer - 1)) * 7) for i in range(n_explorer)
] # apex paper
n_queue = n_explorer
n_queue += 1 # for evaluation
# n_queue += 1 # for prefetch
queues = [manager.Queue() for _ in range(n_queue)]
# Event object to share training status. if event is set True, all exolorers stop sampling transitions
is_training_done = Event()
PER_e = 0.01 # epsilon -> pi = |delta| + epsilon transitions which have zero error also have chance to be selected
PER_a = 0.6 # P(i) = p(i) ** a / total_priority ** a
env_dict = {
"obs": {
"shape": (state_size, )
},
"act": {},
"rew": {},
"next_obs": {
"shape": (state_size, )
},
"done": {}
}
global_rb = manager.PrioritizedReplayBuffer(memory_size,
env_dict=env_dict,
alpha=PER_a,
eps=PER_e)
n_explorer = multiprocessing.cpu_count() - 1
n_queue = n_explorer
n_queue += 1 # for evaluation
queues = [manager.Queue() for _ in range(n_queue)]
# Event object to share training status. if event is set True, all exolorers stop sampling transitions
is_training_done = Event()
# Lock
lock = manager.Lock()
# Shared memory objects to count number of samples and applied gradients
