def env_creator():
if args.game.__package__.endswith('atari'):
if (args.game_name.startswith('foozpong') or
args.game_name.startswith('basketball_pong') or
args.game_name.startswith('volleyball_pong')
):
env = args.game.env(obs_type=args.atari_obs_type,
max_cycles=args.max_steps['atari'],
full_action_space=False,
num_players=2)
else:
env = args.game.env(obs_type=args.atari_obs_type,
full_action_space=False,
max_cycles=args.max_steps['atari'])
env = frame_skip_v0(env, args.atari_frame_skip_num)
env = frame_stack_v1(env, args.atari_frame_stack_num)
else:
env = args.game.env()
if args.game_name.startswith('rps'):
env = one_hot_obs_wrapper(env)
env = dtype_v0(env, dtype=float32)
env = pad_observations_v0(env)
env = pad_action_space_v0(env)
if args.game_name.startswith('connect_four') or args.game_name.startswith('tictactoe'):
env = FlattenEnvWrapper(env)
GAUSSIAN_STD = 1.0
assert abs(GAUSSIAN_STD - 1.0) < 1e-5, "must be 1.0, otherwise simple ensemble implementation is wrong"
env = LatentGaussianAugmentedEnvWrapper(env,
latent_parameter_dim=args.latent_para_dim,
gaussian_std=1.0,
use_dict_obs_space=args.use_dict_obs_space)
return env
def run_parallel2(args):
"""
Test parallel mode with supersuit env wrappers.
"""
parallel_env = eval(args.env).parallel_env()
# as per openai baseline's MaxAndSKip wrapper, maxes over the last 2 frames
# to deal with frame flickering
env = supersuit.max_observation_v0(parallel_env, 2)
# repeat_action_probability is set to 0.25 to introduce non-determinism to the system
env = supersuit.sticky_actions_v0(env, repeat_action_probability=0.25)
# skip frames for faster processing and less control
# to be compatable with gym, use frame_skip(env, (2,5))
env = supersuit.frame_skip_v0(env, 4)
# downscale observation for faster processing
env = supersuit.resize_v0(env, 84, 84)
# allow agent to see everything on the screen despite Atari's flickering screen problem
parallel_env = supersuit.frame_stack_v1(env, 4)
parallel_env.seed(1)
observations = parallel_env.reset()
print(parallel_env.agents)
max_cycles = 500
for step in range(max_cycles):
actions = {agent: 1 for agent in parallel_env.agents}
observations, rewards, dones, infos = parallel_env.step(actions)
parallel_env.render()
def atari_preprocessing(
env: Union[ParallelEnvWrapper, SequentialEnvWrapper]
) -> Union[ParallelEnvWrapper, SequentialEnvWrapper]:
# Preprocessing
env = supersuit.max_observation_v0(env, 2)
# repeat_action_probability is set to 0.25
# to introduce non-determinism to the system
env = supersuit.sticky_actions_v0(env, repeat_action_probability=0.25)
# skip frames for faster processing and less control
# to be compatable with gym, use frame_skip(env, (2,5))
env = supersuit.frame_skip_v0(env, 4)
# downscale observation for faster processing
env = supersuit.resize_v0(env, 84, 84)
# allow agent to see everything on the screen
# despite Atari's flickering screen problem
env = supersuit.frame_stack_v1(env, 4)
# set dtype to float32
env = supersuit.dtype_v0(env, np.float32)
return env
def test_frame_skip():
base_env = DummyEnv(base_obs, base_obs_space, base_act_spaces)
env = supersuit.frame_skip_v0(base_env, 3)
env.reset()
for a in env.agent_iter(8):
obs, rew, done, info = env.last()
env.step(0 if not done else None)
def make_env(env_name='boxing_v1', seed=1, obs_type='rgb_image'):
'''https://www.pettingzoo.ml/atari'''
if env_name == 'slimevolley_v0':
env = SlimeVolleyWrapper(gym.make("SlimeVolley-v0"))
else: # PettingZoo envs
env = eval(env_name).parallel_env(obs_type=obs_type)
if obs_type == 'rgb_image':
# as per openai baseline's MaxAndSKip wrapper, maxes over the last 2 frames
# to deal with frame flickering
env = supersuit.max_observation_v0(env, 2)
# repeat_action_probability is set to 0.25 to introduce non-determinism to the system
env = supersuit.sticky_actions_v0(env,
repeat_action_probability=0.25)
# skip frames for faster processing and less control
# to be compatable with gym, use frame_skip(env, (2,5))
env = supersuit.frame_skip_v0(env, 4)
# downscale observation for faster processing
env = supersuit.resize_v0(env, 84, 84)
# allow agent to see everything on the screen despite Atari's flickering screen problem
env = supersuit.frame_stack_v1(env, 4)
else:
env = supersuit.frame_skip_v0(env, 4)
# env = PettingZooWrapper(env) # need to be put at the end
if env_name in AtariEnvs: # normalize the observation of Atari for both image or RAM
env = supersuit.dtype_v0(
env, 'float32'
) # need to transform uint8 to float first for normalizing observation: https://github.com/PettingZoo-Team/SuperSuit
env = supersuit.normalize_obs_v0(
env, env_min=0,
env_max=1) # normalize the observation to (0,1)
# assign observation and action spaces
env.observation_space = list(env.observation_spaces.values())[0]
env.action_space = list(env.action_spaces.values())[0]
env.seed(seed)
return env
def test_pettinzoo_frame_skip():
# this is the actual frame_skip_v0 test
env = simple_push_v0.raw_env()
env = frame_skip_v0(env, 3)
env.reset()
for x in range(10):
assert env.env.env.steps == (x // 2) * 3
action = env.action_spaces[env.agent_selection].sample()
next_obs = env.step(action)
def test_pettinzoo_frame_skip():
env = simple_push_v2.raw_env(max_cycles=100)
env = frame_skip_v0(env, 3)
env.reset()
x = 0
for agent in env.agent_iter(25):
assert env.env.steps == (x // 2) * 3
action = env.action_spaces[env.agent_selection].sample()
next_obs = env.step(action)
x += 1
def env_creator(args):
env = game_env.env(obs_type='grayscale_image')
#env = clip_reward_v0(env, lower_bound=-1, upper_bound=1)
env = sticky_actions_v0(env, repeat_action_probability=0.25)
env = resize_v0(env, 84, 84)
#env = color_reduction_v0(env, mode='full')
env = frame_skip_v0(env, 4)
env = frame_stack_v1(env, 4)
env = agent_indicator_v0(env, type_only=False)
return env
def _load_env(self, env_name, pettingzoo_params):
from pettingzoo import atari
from supersuit import resize_v0, frame_skip_v0, reshape_v0, max_observation_v0
env = importlib.import_module(
'pettingzoo.atari.{}'.format(env_name)).env(
obs_type='grayscale_image', **pettingzoo_params)
env = max_observation_v0(env, 2)
env = frame_skip_v0(env, 4)
env = resize_v0(env, 84, 84)
env = reshape_v0(env, (1, 84, 84))
return env
def get_env(config):
name = env_name.replace('-', '_')
env = __import__(f'pettingzoo.atari.{name}', fromlist=[None])
env = env.parallel_env(obs_type='grayscale_image')
env = frame_skip_v0(env, 4)
env = resize_v0(env, 84, 84)
env = frame_stack_v1(env, 4)
env = agent_indicator_v0(env)
return ParallelPettingZooEnv(
env,
random_action=config['random_action'],
random_proba=config['random_action_probability'])
def wrap_env(env, obs_type='ram'):
env = env.parallel_env(obs_type=obs_type)
env_agents = env.unwrapped.agents
if obs_type == 'rgb_image':
env = supersuit.max_observation_v0(
env, 2
) # as per openai baseline's MaxAndSKip wrapper, maxes over the last 2 frames to deal with frame flickering
env = supersuit.sticky_actions_v0(
env, repeat_action_probability=0.25
) # repeat_action_probability is set to 0.25 to introduce non-determinism to the system
env = supersuit.frame_skip_v0(
env, 4
) # skip frames for faster processing and less control to be compatable with gym, use frame_skip(env, (2,5))
env = supersuit.resize_v0(
env, 84, 84) # downscale observation for faster processing
env = supersuit.frame_stack_v1(
env, 4
) # allow agent to see everything on the screen despite Atari's flickering screen problem
else:
env = supersuit.frame_skip_v0(
env, 4
) # RAM version also need frame skip, essential for boxing-v1, etc
# normalize the observation of Atari for both image or RAM
env = supersuit.dtype_v0(
env, 'float32'
) # need to transform uint8 to float first for normalizing observation: https://github.com/PettingZoo-Team/SuperSuit
env = supersuit.normalize_obs_v0(
env, env_min=0, env_max=1) # normalize the observation to (0,1)
env.observation_space = list(env.observation_spaces.values())[0]
env.action_space = list(env.action_spaces.values())[0]
env.agents = env_agents
env = Dict2TupleWrapper(env)
return env
def unwrapped_check(env):
# image observations
if isinstance(env.observation_space, spaces.Box):
if ((env.observation_space.low.shape == 3)
and (env.observation_space.low == 0).all()
and (len(env.observation_space.shape[2]) == 3)
and (env.observation_space.high == 255).all()):
env = max_observation_v0(env, 2)
env = color_reduction_v0(env, mode="full")
env = normalize_obs_v0(env)
# box action spaces
if isinstance(env.action_space, spaces.Box):
env = clip_actions_v0(env)
env = scale_actions_v0(env, 0.5)
# stackable observations
if isinstance(env.observation_space, spaces.Box) or isinstance(
env.observation_space, spaces.Discrete):
env = frame_stack_v1(env, 2)
# not discrete and not multibinary observations
if not isinstance(env.observation_space,
spaces.Discrete) and not isinstance(
env.observation_space, spaces.MultiBinary):
env = dtype_v0(env, np.float16)
env = flatten_v0(env)
env = frame_skip_v0(env, 2)
# everything else
env = clip_reward_v0(env, lower_bound=-1, upper_bound=1)
env = delay_observations_v0(env, 2)
env = sticky_actions_v0(env, 0.5)
env = nan_random_v0(env)
env = nan_zeros_v0(env)
assert env.unwrapped.__class__ == DummyEnv, f"Failed to unwrap {env}"
def unwrapped_check(env):
env.reset()
agents = env.agents
if image_observation(env, agents):
env = max_observation_v0(env, 2)
env = color_reduction_v0(env, mode="full")
env = normalize_obs_v0(env)
if box_action(env, agents):
env = clip_actions_v0(env)
env = scale_actions_v0(env, 0.5)
if observation_homogenizable(env, agents):
env = pad_observations_v0(env)
env = frame_stack_v1(env, 2)
env = agent_indicator_v0(env)
env = black_death_v3(env)
if (not_dict_observation(env, agents)
and not_discrete_observation(env, agents)
and not_multibinary_observation(env, agents)):
env = dtype_v0(env, np.float16)
env = flatten_v0(env)
env = frame_skip_v0(env, 2)
if action_homogenizable(env, agents):
env = pad_action_space_v0(env)
env = clip_reward_v0(env, lower_bound=-1, upper_bound=1)
env = delay_observations_v0(env, 2)
env = sticky_actions_v0(env, 0.5)
env = nan_random_v0(env)
env = nan_zeros_v0(env)
assert env.unwrapped.__class__ == DummyEnv, f"Failed to unwrap {env}"
env_min=-1,
env_max=5.0),
supersuit.frame_stack_v1(combined_arms_v6.env(), 8),
supersuit.pad_observations_v0(simple_world_comm_v2.env()),
supersuit.pad_action_space_v0(simple_world_comm_v2.env()),
supersuit.black_death_v3(combined_arms_v6.env()),
supersuit.agent_indicator_v0(knights_archers_zombies_v10.env(), True),
supersuit.agent_indicator_v0(knights_archers_zombies_v10.env(), False),
supersuit.reward_lambda_v0(knights_archers_zombies_v10.env(),
lambda x: x / 10),
supersuit.clip_reward_v0(combined_arms_v6.env()),
supersuit.nan_noop_v0(knights_archers_zombies_v10.env(), 0),
supersuit.nan_zeros_v0(knights_archers_zombies_v10.env()),
supersuit.nan_random_v0(chess_v5.env()),
supersuit.nan_random_v0(knights_archers_zombies_v10.env()),
supersuit.frame_skip_v0(combined_arms_v6.env(), 4),
supersuit.sticky_actions_v0(combined_arms_v6.env(), 0.75),
supersuit.delay_observations_v0(combined_arms_v6.env(), 3),
supersuit.max_observation_v0(knights_archers_zombies_v10.env(), 3),
]
@pytest.mark.parametrize("env", wrappers)
def test_pettingzoo_aec_api(env):
api_test(env)
parallel_wrappers = [
supersuit.frame_stack_v1(combined_arms_v6.parallel_env(), 8),
supersuit.frame_stack_v1(simple_push_v2.parallel_env(), 8),
supersuit.reward_lambda_v0(combined_arms_v6.parallel_env(),
supersuit.reshape_v0(knights_archers_zombies_v4.env(), (512 * 512, 3)),
supersuit.normalize_obs_v0(dtype_v0(knights_archers_zombies_v4.env(),
np.float32),
env_min=-1,
env_max=5.0),
supersuit.frame_stack_v1(knights_archers_zombies_v4.env(), 8),
supersuit.pad_observations_v0(knights_archers_zombies_v4.env()),
supersuit.pad_action_space_v0(knights_archers_zombies_v4.env()),
supersuit.black_death_v0(knights_archers_zombies_v4.env()),
supersuit.agent_indicator_v0(knights_archers_zombies_v4.env(), True),
supersuit.agent_indicator_v0(knights_archers_zombies_v4.env(), False),
supersuit.reward_lambda_v0(knights_archers_zombies_v4.env(),
lambda x: x / 10),
supersuit.clip_reward_v0(knights_archers_zombies_v4.env()),
supersuit.clip_actions_v0(prison_v2.env(continuous=True)),
supersuit.frame_skip_v0(knights_archers_zombies_v4.env(), 4),
supersuit.sticky_actions_v0(knights_archers_zombies_v4.env(), 0.75),
supersuit.delay_observations_v0(knights_archers_zombies_v4.env(), 3),
]
@pytest.mark.parametrize("env", wrappers)
def test_pettingzoo_aec_api(env):
api_test.api_test(env)
parallel_wrappers = [
supersuit.frame_stack_v1(knights_archers_zombies_v4.parallel_env(), 8),
supersuit.reward_lambda_v0(knights_archers_zombies_v4.parallel_env(),
lambda x: x / 10),
supersuit.delay_observations_v0(knights_archers_zombies_v4.parallel_env(),
def create_single_env(args):
env_name = args.env
if args.num_envs > 1:
keep_info = True # keep_info True to maintain dict type for parallel envs (otherwise cannot pass VectorEnv wrapper)
else:
keep_info = False
'''https://www.pettingzoo.ml/atari'''
if "slimevolley" in env_name or "SlimeVolley" in env_name:
print(f'Load SlimeVolley env: {env_name}')
env = gym.make(env_name)
if env_name in [
'SlimeVolleySurvivalNoFrameskip-v0',
'SlimeVolleyNoFrameskip-v0', 'SlimeVolleyPixel-v0'
]:
# For image-based envs, apply following wrappers (from gym atari) to achieve pettingzoo style env,
# or use supersuit (requires input env to be either pettingzoo or gym env).
# same as: https://github.com/hardmaru/slimevolleygym/blob/master/training_scripts/train_ppo_pixel.py
# TODO Note: this cannot handle the two obervations in above SlimeVolley envs,
# since the wrappers are for single agent.
if env_name != 'SlimeVolleyPixel-v0':
env = NoopResetEnv(env, noop_max=30)
env = MaxAndSkipEnv(env, skip=4)
env = WarpFrame(env)
# #env = ClipRewardEnv(env)
env = FrameStack(env, 4)
env = SlimeVolleyWrapper(
env, args.against_baseline) # slimevolley to pettingzoo style
env = NFSPPettingZooWrapper(
env, keep_info=keep_info
) # pettingzoo to nfsp style, keep_info True to maintain dict type for parallel envs
elif env_name in AtariEnvs: # PettingZoo Atari envs
print(f'Load PettingZoo Atari env: {env_name}')
if args.ram:
obs_type = 'ram'
else:
obs_type = 'rgb_image'
env = eval(env_name).parallel_env(obs_type=obs_type)
env_agents = env.unwrapped.agents # this cannot go through supersuit wrapper, so get it first and reassign it
if obs_type == 'rgb_image':
# as per openai baseline's MaxAndSKip wrapper, maxes over the last 2 frames
# to deal with frame flickering
env = supersuit.max_observation_v0(env, 2)
# repeat_action_probability is set to 0.25 to introduce non-determinism to the system
env = supersuit.sticky_actions_v0(env,
repeat_action_probability=0.25)
# skip frames for faster processing and less control
# to be compatable with gym, use frame_skip(env, (2,5))
env = supersuit.frame_skip_v0(env, 4)
# downscale observation for faster processing
env = supersuit.resize_v0(env, 84, 84)
# allow agent to see everything on the screen despite Atari's flickering screen problem
env = supersuit.frame_stack_v1(env, 4)
else:
env = supersuit.frame_skip_v0(
env, 4
) # RAM version also need frame skip, essential for boxing-v1, etc
# env = PettingZooWrapper(env) # need to be put at the end
# normalize the observation of Atari for both image or RAM
env = supersuit.dtype_v0(
env, 'float32'
) # need to transform uint8 to float first for normalizing observation: https://github.com/PettingZoo-Team/SuperSuit
env = supersuit.normalize_obs_v0(
env, env_min=0, env_max=1) # normalize the observation to (0,1)
# assign observation and action spaces
env.observation_space = list(env.observation_spaces.values())[0]
env.action_space = list(env.action_spaces.values())[0]
env.agents = env_agents
env = NFSPPettingZooWrapper(
env, keep_info=keep_info
) # pettingzoo to nfsp style, keep_info True to maintain dict type for parallel envs)
elif env_name in ClassicEnvs: # PettingZoo Classic envs
print(f'Load PettingZoo Classic env: {env_name}')
if env_name in ['rps_v1', 'rpsls_v1']:
env = eval(env_name).parallel_env()
env = PettingzooClassicWrapper(env, observation_mask=1.)
else: # only rps_v1 can use parallel_env at present
env = eval(env_name).env()
env = PettingzooClassic_Iterate2Parallel(
env, observation_mask=None
) # since Classic games do not support Parallel API yet
env = NFSPPettingZooWrapper(env, keep_info=keep_info)
elif "LaserTag" in env_name: # LaserTag: https://github.com/younggyoseo/pytorch-nfsp
print(f'Load LaserTag env: {env_name}')
env = gym.make(env_name)
env = wrap_pytorch(env)
else: # gym env
print(f'Load Gym env: {env_name}')
try:
env = gym.make(env_name)
except:
print(f"Error: No such env: {env_name}!")
# may need more wrappers here, e.g. Pong-ram-v0 need scaled observation!
# Ref: https://towardsdatascience.com/deep-q-network-dqn-i-bce08bdf2af
env = NFSPAtariWrapper(env, keep_info=keep_info)
env.seed(args.seed)
return env
wrappers = [
supersuit.dtype_v0(generated_agents_parallel_v0.env(), np.int32),
supersuit.flatten_v0(generated_agents_parallel_v0.env()),
supersuit.normalize_obs_v0(
dtype_v0(generated_agents_parallel_v0.env(), np.float32),
env_min=-1,
env_max=5.0,
),
supersuit.frame_stack_v1(generated_agents_parallel_v0.env(), 8),
supersuit.reward_lambda_v0(generated_agents_parallel_v0.env(),
lambda x: x / 10),
supersuit.clip_reward_v0(generated_agents_parallel_v0.env()),
supersuit.nan_noop_v0(generated_agents_parallel_v0.env(), 0),
supersuit.nan_zeros_v0(generated_agents_parallel_v0.env()),
supersuit.nan_random_v0(generated_agents_parallel_v0.env()),
supersuit.frame_skip_v0(generated_agents_parallel_v0.env(), 4),
supersuit.sticky_actions_v0(generated_agents_parallel_v0.env(), 0.75),
supersuit.delay_observations_v0(generated_agents_parallel_v0.env(), 3),
supersuit.max_observation_v0(generated_agents_parallel_v0.env(), 3),
]
@pytest.mark.parametrize("env", wrappers)
def test_pettingzoo_aec_api_par_gen(env):
api_test(env, num_cycles=50)
wrappers = [
supersuit.dtype_v0(generated_agents_env_v0.env(), np.int32),
supersuit.flatten_v0(generated_agents_env_v0.env()),
supersuit.normalize_obs_v0(dtype_v0(generated_agents_env_v0.env(),
def test_frame_skip():
base_env = DummyEnv(base_obs, base_obs_space, base_act_spaces)
env = supersuit.frame_skip_v0(base_env, 3)
env.reset()
for i in range(10):
env.step(0)
return DummyEnv(base_obs, base_obs_space, base_act_spaces)
wrappers = [
supersuit.color_reduction_v0(new_dummy(), "R"),
supersuit.resize_v0(dtype_v0(new_dummy(), np.uint8), x_size=5, y_size=10),
supersuit.resize_v0(dtype_v0(new_dummy(), np.uint8), x_size=5, y_size=10, linear_interp=True),
supersuit.dtype_v0(new_dummy(), np.int32),
supersuit.flatten_v0(new_dummy()),
supersuit.reshape_v0(new_dummy(), (64, 3)),
supersuit.normalize_obs_v0(new_dummy(), env_min=-1, env_max=5.0),
supersuit.frame_stack_v1(new_dummy(), 8),
supersuit.reward_lambda_v0(new_dummy(), lambda x: x / 10),
supersuit.clip_reward_v0(new_dummy()),
supersuit.clip_actions_v0(new_continuous_dummy()),
supersuit.frame_skip_v0(new_dummy(), 4),
supersuit.frame_skip_v0(new_dummy(), (4, 6)),
supersuit.sticky_actions_v0(new_dummy(), 0.75),
supersuit.delay_observations_v0(new_dummy(), 1),
]
@pytest.mark.parametrize("env", wrappers)
def test_basic_wrappers(env):
env.seed(5)
obs = env.reset()
act_space = env.action_space
obs_space = env.observation_space
assert obs_space.contains(obs)
assert obs.dtype == obs_space.dtype
for i in range(10):
device = torch.device(
'cuda' if torch.cuda.is_available() and args.cuda else 'cpu')
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.backends.cudnn.deterministic = args.torch_deterministic
# https://github.com/cpnota/autonomous-learning-library/blob/5ee29eac4ad22d6de00f89345dce6f9c55569149/all/environments/multiagent_atari.py#L26
# def make_atari(env_name, pettingzoo_params):
env = importlib.import_module(
args.gym_id).parallel_env(obs_type='grayscale_image')
env = ss.agent_indicator_v0(env)
env = ss.clip_reward_v0(env)
env = max_observation_v0(env, 2)
env = frame_skip_v0(env, 4)
env = resize_v0(env, 84, 84)
env = ss.pettingzoo_env_to_vec_env_v0(env)
envs = ss.concat_vec_envs_v0(env,
args.num_envs,
num_cpus=args.num_envs,
base_class='stable_baselines3')
envs = VecMonitor(envs)
if args.capture_video:
envs = VecVideoRecorder(envs,
f'videos/{experiment_name}',
record_video_trigger=lambda x: x % 150000 == 0,
video_length=400)
envs = VecPyTorch(envs, device)
args.num_envs = envs.num_envs
args.batch_size = int(args.num_envs * args.num_steps)
