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) #env = flatten_v0(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}"
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), ] @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)
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()), 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(),
import supersuit from supersuit import dtype_v0 import pytest 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)
supersuit.flatten_v0(knights_archers_zombies_v10.env()), supersuit.reshape_v0(knights_archers_zombies_v10.env(vector_state=False), (512 * 512, 3)), supersuit.normalize_obs_v0(dtype_v0(knights_archers_zombies_v10.env(), np.float32), 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)
# 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 # 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)