json.dump(vars(args), f)
pprint(vars(args))
if not os.path.exists(os.path.join(args.log, 'models')):
os.mkdir(os.path.join(args.log, 'models'))
np.random.seed(args.seed)
torch.manual_seed(args.seed)
device_name = 'cpu' if args.cuda < 0 else "cuda:{}".format(args.cuda)
device = torch.device(device_name)
set_device(device)
score_file = os.path.join(args.log, 'progress.csv')
logger.add_tabular_output(score_file)
logger.add_tensorboard_output(args.log)
env = GymEnv(
args.env_name,
log_dir=os.path.join(
args.log,
'movie'),
record_video=args.record)
env.env.seed(args.seed)
if args.c2d:
env = C2DEnv(env)
observation_space = env.observation_space
action_space = env.action_space
# Generate teacher (t) policy and student (s) policy and load teacher policy
def main(args):
init_ray(args.num_cpus, args.num_gpus, args.ray_redis_address)
if not os.path.exists(args.log):
os.makedirs(args.log)
if not os.path.exists(os.path.join(args.log, 'models')):
os.mkdir(os.path.join(args.log, 'models'))
score_file = os.path.join(args.log, 'progress.csv')
logger.add_tabular_output(score_file)
logger.add_tensorboard_output(args.log)
with open(os.path.join(args.log, 'args.json'), 'w') as f:
json.dump(vars(args), f)
pprint(vars(args))
# when doing the distributed training, disable video recordings
env = GymEnv(args.env_name)
env.env.seed(args.seed)
if args.c2d:
env = C2DEnv(env)
observation_space = env.observation_space
action_space = env.action_space
pol_net = PolNet(observation_space, action_space)
rnn = False
# pol_net = PolNetLSTM(observation_space, action_space)
# rnn = True
if isinstance(action_space, gym.spaces.Box):
pol = GaussianPol(observation_space, action_space, pol_net, rnn=rnn)
elif isinstance(action_space, gym.spaces.Discrete):
pol = CategoricalPol(observation_space, action_space, pol_net)
elif isinstance(action_space, gym.spaces.MultiDiscrete):
pol = MultiCategoricalPol(observation_space, action_space, pol_net)
else:
raise ValueError('Only Box, Discrete, and MultiDiscrete are supported')
vf_net = VNet(observation_space)
vf = DeterministicSVfunc(observation_space, vf_net)
trainer = TrainManager(Trainer,
args.num_trainer,
args.master_address,
args=args,
vf=vf,
pol=pol)
sampler = EpiSampler(env, pol, args.num_parallel, seed=args.seed)
total_epi = 0
total_step = 0
max_rew = -1e6
start_time = time.time()
while args.max_epis > total_epi:
with measure('sample'):
sampler.set_pol_state(trainer.get_state("pol"))
epis = sampler.sample(max_steps=args.max_steps_per_iter)
with measure('train'):
result_dict = trainer.train(epis=epis)
step = result_dict["traj_num_step"]
total_step += step
total_epi += result_dict["traj_num_epi"]
rewards = [np.sum(epi['rews']) for epi in epis]
mean_rew = np.mean(rewards)
elapsed_time = time.time() - start_time
logger.record_tabular('ElapsedTime', elapsed_time)
logger.record_results(args.log,
result_dict,
score_file,
total_epi,
step,
total_step,
rewards,
plot_title=args.env_name)
with measure('save'):
pol_state = trainer.get_state("pol")
vf_state = trainer.get_state("vf")
optim_pol_state = trainer.get_state("optim_pol")
optim_vf_state = trainer.get_state("optim_vf")
torch.save(pol_state,
os.path.join(args.log, 'models', 'pol_last.pkl'))
torch.save(vf_state, os.path.join(args.log, 'models',
'vf_last.pkl'))
torch.save(optim_pol_state,
os.path.join(args.log, 'models', 'optim_pol_last.pkl'))
torch.save(optim_vf_state,
os.path.join(args.log, 'models', 'optim_vf_last.pkl'))
if mean_rew > max_rew:
torch.save(pol_state,
os.path.join(args.log, 'models', 'pol_max.pkl'))
torch.save(vf_state,
os.path.join(args.log, 'models', 'vf_max.pkl'))
torch.save(
optim_pol_state,
os.path.join(args.log, 'models', 'optim_pol_max.pkl'))
torch.save(
optim_vf_state,
os.path.join(args.log, 'models', 'optim_vf_max.pkl'))
max_rew = mean_rew
del sampler
del trainer
