self.max_episode_length = 10000
self.seed = 1
self.env_name = 'Pendulum-v0'
if __name__ == '__main__':
os.environ['OMP_NUM_THREADS'] = '1'
params = Params()
torch.manual_seed(params.seed)
env = gym.make(params.env_name)
num_inputs = env.observation_space.shape[0]
num_outputs = env.action_space.shape[0]
shared_p = Policy(num_inputs, num_outputs)
shared_v = Value(num_inputs)
shared_p.share_memory()
shared_v.share_memory()
optimizer_p = my_optim.SharedAdam(shared_p.parameters(), lr=params.lr)
optimizer_v = my_optim.SharedAdam(shared_v.parameters(), lr=params.lr)
processes = []
p = mp.Process(target=test, args=(params.num_processes, params, shared_p))
p.start()
processes.append(p)
for rank in range(0, params.num_processes):
p = mp.Process(target=train,
args=(rank, params, shared_p, shared_v, optimizer_p,
optimizer_v))
p.start()
processes.append(p)
for p in processes:
# Getting action dim and observation dim from Env
env = Env(args, device='cpu', options=options, dummy=True)
observation_dim = env.observation_dim
args.action_dim = env.action_dim
env.close()
print('Observation Space: {} / Action Dim: {}'.format(
observation_dim, args.action_dim))
# Initializing shared memory used between workers and learner that contains the actor parameters
shared_state_dict = Policy(args.action_dim)
if args.load_model is not None:
partial_load(shared_state_dict, args.load_model)
if args.reset_policy:
shared_state_dict.policy.weight.data.zero_()
shared_state_dict.policy.bias.data.zero_()
shared_state_dict = shared_state_dict.share_memory()
# Creating learner
learner = Learner(args, experience_buffer.queue_batch, shared_state_dict)
# Creating actors
actors = []
for i in range(args.n_workers):
actor_name = 'actor_' + str(i)
actor = Actor(args,
experience_buffer.queue_trace,
shared_state_dict,
actor_name,
rank=i)
actors.append(actor)
