def make_env(game_name):
env = gym.make(
game_name
) # Already performs a frame-skip of 4 @ baselines.common.atari_wrappers_deprecated
monitored_env = SimpleMonitor(
env
) # puts rewards and number of steps in info, before environment is wrapped
#env = wrap_dqn(monitored_env) # applies a bunch of modification to simplify the observation space (downsample, make b/w)
return env, monitored_env
def make_env(game_name):
env = gym.make(game_name + "NoFrameskip-v4")
monitored_env = SimpleMonitor(
env
) # puts rewards and number of steps in info, before environment is wrapped
env = wrap_dqn(
monitored_env
) # applies a bunch of modification to simplify the observation space (downsample, make b/w)
return env, monitored_env
def make_env(game_name):
env = gym.make(game_name + "NoFrameskip-v4")
env = SimpleMonitor(env)
env = wrap_dqn(env)
return env
def run(env_id, seed, noise_type, num_cpu, layer_norm, logdir, gym_monitor, evaluation, bind_to_core, **kwargs):
kwargs['logdir'] = logdir
whoami = mpi_fork(num_cpu, bind_to_core=bind_to_core)
if whoami == 'parent':
sys.exit(0)
# Configure things.
rank = MPI.COMM_WORLD.Get_rank()
if rank != 0:
# Write to temp directory for all non-master workers.
actual_dir = None
Logger.CURRENT.close()
Logger.CURRENT = Logger(dir=mkdtemp(), output_formats=[])
logger.set_level(logger.DISABLED)
# Create envs.
if rank == 0:
env = gym.make(env_id)
if gym_monitor and logdir:
env = gym.wrappers.Monitor(env, os.path.join(logdir, 'gym_train'), force=True)
env = SimpleMonitor(env)
if evaluation:
eval_env = gym.make(env_id)
if gym_monitor and logdir:
eval_env = gym.wrappers.Monitor(eval_env, os.path.join(logdir, 'gym_eval'), force=True)
eval_env = SimpleMonitor(eval_env)
else:
eval_env = None
else:
env = gym.make(env_id)
if evaluation:
eval_env = gym.make(env_id)
else:
eval_env = None
# Parse noise_type
action_noise = None
param_noise = None
nb_actions = env.action_space.shape[-1]
for current_noise_type in noise_type.split(','):
current_noise_type = current_noise_type.strip()
if current_noise_type == 'none':
pass
elif 'adaptive-param' in current_noise_type:
_, stddev = current_noise_type.split('_')
param_noise = AdaptiveParamNoiseSpec(initial_stddev=float(stddev), desired_action_stddev=float(stddev))
elif 'normal' in current_noise_type:
_, stddev = current_noise_type.split('_')
action_noise = NormalActionNoise(mu=np.zeros(nb_actions), sigma=float(stddev) * np.ones(nb_actions))
elif 'ou' in current_noise_type:
_, stddev = current_noise_type.split('_')
action_noise = OrnsteinUhlenbeckActionNoise(mu=np.zeros(nb_actions), sigma=float(stddev) * np.ones(nb_actions))
else:
raise RuntimeError('unknown noise type "{}"'.format(current_noise_type))
# Configure components.
memory = Memory(limit=int(1e6), action_shape=env.action_space.shape, observation_shape=env.observation_space.shape)
critic = Critic(layer_norm=layer_norm)
actor = Actor(nb_actions, layer_norm=layer_norm)
# Seed everything to make things reproducible.
seed = seed + 1000000 * rank
logger.info('rank {}: seed={}, logdir={}'.format(rank, seed, logger.get_dir()))
tf.reset_default_graph()
set_global_seeds(seed)
env.seed(seed)
if eval_env is not None:
eval_env.seed(seed)
# Disable logging for rank != 0 to avoid noise.
if rank == 0:
start_time = time.time()
training.train(env=env, eval_env=eval_env, param_noise=param_noise,
action_noise=action_noise, actor=actor, critic=critic, memory=memory, **kwargs)
env.close()
if eval_env is not None:
eval_env.close()
Logger.CURRENT.close()
if rank == 0:
logger.info('total runtime: {}s'.format(time.time() - start_time))
def make_env(game_name):
env = gym.make(game_name + "NoFrameskip-v3")
env_monitored = SimpleMonitor(env)
env = wrap_dqn(env_monitored)
return env_monitored, env
def make_env(game_name):
env = gym.make(game_name + "NoFrameskip-v4")
env = bench.Monitor(env, None)
env = SimpleMonitor(env)
env = wrap_atari_dqn(env)
return env
except Exception as e:
sys.exit(
str(e) + '\n' +
'map_config import error. File not exist or map_config not specified'
)
from gym.envs.registration import register
register(
id='MonsterKong-v0',
entry_point='baselines.ple.gym_env.monsterkong:MonsterKongEnv',
kwargs={'map_config': map_config},
)
env = gym.make('MonsterKong-v0')
env = ProcessFrame(env)
env = SimpleMonitor(env)
else:
env, monitored_env = make_env(args.env)
subdir = (datetime.datetime.now()
).strftime("%m-%d-%Y-%H:%M:%S") + " " + args.comment
tf_writer = tf.summary.FileWriter(os.path.join(args.log_dir, subdir),
tf.get_default_graph())
value_summary = tf.Summary()
qec_summary = tf.Summary()
value_summary.value.add(tag='discount_reward_mean')
value_summary.value.add(tag='non_discount_reward_mean')
# value_summary.value.add(tag='non_discount_reward_mean')
# value_summary.value.add(tag='episode')
qec_summary.value.add(tag='qec_mean')