def make_env(process_idx, test):
env = gym.make(args.env)
# Unwrap TimiLimit wrapper
assert isinstance(env, gym.wrappers.TimeLimit)
env = env.env
# Use different random seeds for train and test envs
process_seed = int(process_seeds[process_idx])
env_seed = 2**32 - 1 - process_seed if test else process_seed
env.seed(env_seed)
if isinstance(env.observation_space, Box):
# Cast observations to float32 because our model uses float32
env = chainerrl.wrappers.CastObservationToFloat32(env)
else:
env = atari_wrappers.wrap_deepmind(atari_wrappers.make_atari(
args.env, max_frames=None),
episode_life=not test,
clip_rewards=not test)
if isinstance(env.action_space, Box):
# Normalize action space to [-1, 1]^n
env = wrappers.NormalizeActionSpace(env)
if args.monitor:
env = gym.wrappers.Monitor(env, args.outdir)
if args.render:
env = chainerrl.wrappers.Render(env)
return env
def make_env_check():
# Use different random seeds for train and test envs
env_seed = args.seed
env = atari_wrappers.wrap_deepmind(atari_wrappers.make_atari(
args.env, max_frames=args.max_frames),
episode_life=True,
clip_rewards=True)
env.seed(int(env_seed))
return env
def make_env():
env = atari_wrappers.wrap_deepmind(
atari_wrappers.make_atari(env_name),
episode_life=False,
clip_rewards=False,
)
env.seed(seed)
misc.env_modifiers.make_rendered(env)
return env
def make_env(test):
# Use different random seeds for train and test envs
env_seed = test_seed if test else train_seed
env = atari_wrappers.wrap_deepmind(atari_wrappers.make_atari(args.env),
episode_life=not test,
clip_rewards=not test)
env.seed(int(env_seed))
if args.monitor:
env = gym.wrappers.Monitor(
env, args.outdir, mode='evaluation' if test else 'training')
if args.render:
env = chainerrl.wrappers.Render(env)
return env
def make_env():
env = atari_wrappers.wrap_deepmind(atari_wrappers.make_atari(
args.env, max_frames=None),
episode_life=False,
clip_rewards=False)
env.seed(int(args.seed))
# Randomize actions like epsilon-greedy
env = chainerrl.wrappers.RandomizeAction(env, 0.01)
if args.monitor:
env = gym.wrappers.Monitor(env, args.outdir, mode='evaluation')
if args.render:
env = chainerrl.wrappers.Render(env)
return env
def make_env(process_idx, test):
# Use different random seeds for train and test envs
process_seed = process_seeds[process_idx]
env_seed = 2**31 - 1 - process_seed if test else process_seed
env = atari_wrappers.wrap_deepmind(atari_wrappers.make_atari(
args.env, max_frames=args.max_frames),
episode_life=not test,
clip_rewards=not test)
env.seed(int(env_seed))
if args.monitor:
env = gym.wrappers.Monitor(
env, args.outdir, mode='evaluation' if test else 'training')
if args.render:
env = chainerrl.wrappers.Render(env)
return env
def make_env(test):
# Use different random seeds for train and test envs
env_seed = test_seed if test else train_seed
env = atari_wrappers.wrap_deepmind(atari_wrappers.make_atari(
args.env, max_frames=None),
episode_life=not test,
clip_rewards=not test)
env.seed(int(env_seed))
if test:
# Randomize actions like epsilon-greedy in evaluation as well
env = chainerrl.wrappers.RandomizeAction(env, 0.05)
if args.monitor:
env = chainerrl.wrappers.Monitor(
env, args.outdir, mode='evaluation' if test else 'training')
if args.render:
env = chainerrl.wrappers.Render(env)
return env
def make_env(idx, test):
# Use different random seeds for train and test envs
process_seed = int(process_seeds[idx])
env_seed = 2**32 - 1 - process_seed if test else process_seed
env = atari_wrappers.wrap_deepmind(atari_wrappers.make_atari(args.env),
episode_life=not test,
clip_rewards=not test)
if test:
# Randomize actions like epsilon-greedy in evaluation as well
env = chainerrl.wrappers.RandomizeAction(env, args.eval_epsilon)
env.seed(env_seed)
if args.monitor:
env = gym.wrappers.Monitor(
env, args.outdir, mode='evaluation' if test else 'training')
if args.render:
env = chainerrl.wrappers.Render(env)
return env
def make_env():
env = atari_wrappers.wrap_deepmind(atari_wrappers.make_atari(env_name),
episode_life=False,
clip_rewards=False)
env.seed(seed)
return env
