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 create_envs(self,
n_envs: int,
eval_env: bool = False,
no_log: bool = False) -> VecEnv:
env = pistonball_v5.parallel_env()
env = ss.color_reduction_v0(env, mode="B")
env = ss.resize_v0(env, x_size=84, y_size=84, linear_interp=True)
env = ss.frame_stack_v1(env, 3)
env = ss.pettingzoo_env_to_vec_env_v1(env)
print(n_envs)
env = ss.concat_vec_envs_v1(env,
n_envs,
num_cpus=4,
base_class="stable_baselines3")
env = VecMonitor(env)
env = self._maybe_normalize(env, eval_env)
if is_image_space(
env.observation_space) and not is_image_space_channels_first(
env.observation_space):
if self.verbose > 0:
print("Wrapping into a VecTransposeImage")
env = VecTransposeImage(env)
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 env_creator():
env = pistonball_v4.env(n_pistons=20, local_ratio=0, time_penalty=-0.1, continuous=True, random_drop=True, random_rotate=True, ball_mass=0.75, ball_friction=0.3, ball_elasticity=1.5, max_cycles=125)
env = ss.color_reduction_v0(env, mode='B')
env = ss.dtype_v0(env, 'float32')
env = ss.resize_v0(env, x_size=84, y_size=84)
env = ss.normalize_obs_v0(env, env_min=0, env_max=1)
env = ss.frame_stack_v1(env, 3)
return env
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_v0(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 make_env(env_name):
if env_name == "pistonball":
env = pistonball_v0.parallel_env(max_frames=100)
env = supersuit.resize_v0(env, 16, 16)
env = supersuit.dtype_v0(env, np.float32)
env = supersuit.normalize_obs_v0(env)
return supersuit.flatten_v0(env)
if env_name == "KAZ":
env = knights_archers_zombies_v2.parallel_env(max_frames=100)
env = supersuit.resize_v0(env, 32, 32)
env = supersuit.dtype_v0(env, np.float32)
env = supersuit.normalize_obs_v0(env)
return supersuit.flatten_v0(env)
if env_name == "pursuit":
env = pursuit_v1.parallel_env(max_frames=100)
env = supersuit.resize_v0(env, 32, 32)
return supersuit.flatten_v0(env)
elif env_name == "waterworld":
return waterworld_v1.parallel_env(max_frames=100)
elif env_name == "multiwalker":
return multiwalker_v3.parallel_env(max_frames=100)
else:
raise RuntimeError("bad environment name")
def __init__(self, env_name, device='cpu', frame_skip=4):
env = importlib.import_module(
'pettingzoo.atari.{}'.format(env_name)).parallel_env(
obs_type='grayscale_image')
env = MaxAndSkipMAALE(env, skip=frame_skip)
env = from_parallel(env)
env = resize_v0(env, 84, 84)
self._env = env
self.name = env_name
self.device = torch.device(device)
self.agents = self._env.possible_agents
self.subenvs = {
agent: SubEnv(agent, device, self.state_spaces[agent],
self.action_spaces[agent])
for agent in self.agents
}
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 env_creator(args):
env = env_constr.env(
) #killable_knights=False, killable_archers=False)
resize_size = 84 if model == None else 32
env = supersuit.resize_v0(env,
resize_size,
resize_size,
linear_interp=True)
env = supersuit.color_reduction_v0(env)
env = supersuit.pad_action_space_v0(env)
env = supersuit.pad_observations_v0(env)
# env = supersuit.frame_stack_v0(env,2)
env = supersuit.dtype_v0(env, np.float32)
env = supersuit.normalize_obs_v0(env)
if model == "MLPModelV2":
env = supersuit.flatten_v0(env)
env = PettingZooEnv(env)
return env
def __init__(self, env_name, device='cpu', frame_skip=4):
def raw_env(**kwargs):
return BaseAtariEnv(game="surround",
num_players=1,
mode_num=2,
**kwargs)
env = parallel_wrapper_fn(
base_env_wrapper_fn(raw_env))(obs_type="grayscale_image")
env = MaxAndSkipMAALE(env, skip=frame_skip)
env = from_parallel(env)
env = recolor_observations(env)
env = resize_v0(env, 84, 84)
self._env = env
self.name = env_name
self.device = torch.device(device)
self.agents = self._env.possible_agents
self.subenvs = {
agent: SubEnv(agent, device, self.state_spaces[agent],
self.action_spaces[agent])
for agent in self.agents
}
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
wrapped_env = pad_action_space_v0(_env)
api_test.api_test(wrapped_env)
seed_test.seed_test(
lambda: sticky_actions_v0(simple_world_comm_v2.env(), 0.5), 100)
def test_pettingzoo_parallel_env():
_env = simple_world_comm_v2.parallel_env()
wrapped_env = pad_action_space_v0(_env)
parallel_test.parallel_play_test(wrapped_env)
wrappers = [
supersuit.color_reduction_v0(knights_archers_zombies_v4.env(), "R"),
supersuit.resize_v0(dtype_v0(knights_archers_zombies_v4.env(), np.uint8),
x_size=5,
y_size=10),
supersuit.resize_v0(dtype_v0(knights_archers_zombies_v4.env(), np.uint8),
x_size=5,
y_size=10,
linear_interp=True),
supersuit.dtype_v0(knights_archers_zombies_v4.env(), np.int32),
supersuit.flatten_v0(knights_archers_zombies_v4.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()),
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
first_obs, _, _, _ = env.step(5)
assert np.all(np.equal(first_obs, base_obs.reshape([64, 3])))
def new_continuous_dummy():
base_act_spaces = Box(low=np.float32(0.0), high=np.float32(10.0), shape=[3])
return DummyEnv(base_obs, base_obs_space, base_act_spaces)
def new_dummy():
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),
]
params = json.load(f)
print(params)
def image_transpose(env):
if is_image_space(env.observation_space) and not is_image_space_channels_first(
env.observation_space
):
env = VecTransposeImage(env)
return env
env = pistonball_v5.parallel_env()
env = ss.color_reduction_v0(env, mode="B")
env = ss.resize_v0(env, x_size=84, y_size=84)
env = ss.frame_stack_v1(env, 3)
env = ss.pettingzoo_env_to_vec_env_v1(env)
env = ss.concat_vec_envs_v1(env, n_envs, num_cpus=1, base_class="stable_baselines3")
env = VecMonitor(env)
env = image_transpose(env)
eval_env = pistonball_v5.parallel_env()
eval_env = ss.color_reduction_v0(eval_env, mode="B")
eval_env = ss.resize_v0(eval_env, x_size=84, y_size=84)
eval_env = ss.frame_stack_v1(eval_env, 3)
eval_env = ss.pettingzoo_env_to_vec_env_v1(eval_env)
eval_env = ss.concat_vec_envs_v1(
eval_env, 1, num_cpus=1, base_class="stable_baselines3"
)
eval_env = VecMonitor(eval_env)
eval_freq = max(eval_freq // (n_envs*n_agents), 1)
model = PPO("MlpPolicy", env, verbose=3, gamma=0.95, n_steps=256, ent_coef=0.0905168, learning_rate=0.00062211, vf_coef=0.042202, max_grad_norm=0.9, gae_lambda=0.99, n_epochs=5, clip_range=0.3, batch_size=256)
eval_callback = EvalCallback(eval_env, best_model_save_path='./logs/', log_path='./logs/', eval_freq=eval_freq, deterministic=True, render=False)
model.learn(total_timesteps=n_timesteps, callback=eval_callback)
model = PPO.load("./logs/best_model")
mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10)
print(mean_reward)
print(std_reward)
render_env = base_env.copy().parallel_env()
render_env = ss.color_reduction_v0(render_env, mode='B')
render_env = ss.resize_v0(render_env, x_size=84, y_size=84)
render_env = ss.frame_stack_v1(render_env, 3)
obs_list = []
i = 0
render_env.reset()
while True:
for agent in render_env.agent_iter():
observation, _, done, _ = render_env.last()
action = model.predict(observation, deterministic=True)[0] if not done else None
render_env.step(action)
i += 1
if i % (len(render_env.possible_agents)) == 0:
monitor_gym=True,
save_code=True)
writer = SummaryWriter(f"/tmp/{experiment_name}")
# TRY NOT TO MODIFY: seeding
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
# petting zoo
env = pistonball_v4.parallel_env()
env = ss.color_reduction_v0(env, mode='B')
env = ss.resize_v0(env, x_size=84, y_size=84)
env = ss.frame_stack_v1(env, 3)
env = ss.pettingzoo_env_to_vec_env_v0(env)
envs = ss.concat_vec_envs_v0(env,
args.num_envs,
num_cpus=0,
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)
'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)
args.minibatch_size = int(args.batch_size // args.n_minibatch)
