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 env_fn(): #env = gym.make("CartPole-v0")# env = simple_spread_v2.parallel_env() # print(env.action_spaces.values()) # exit(0) env = supersuit.pad_observations_v0(env) env = supersuit.pad_action_space_v0(env) #env = supersuit.aec_wrappers.continuous_actions(env) venv = pettingzoo_env_to_vec_env_v0(env) return venv
def comm_env(**kwargs): raw_env = base_env.raw_env(**kwargs) # Set all agents to silent for agent in raw_env.world.agents: agent.silent = True env = AssertOutOfBoundsWrapper(raw_env) env = OrderEnforcingWrapper(env) env = CommWrapper(env, comm_dict) env = pad_observations_v0(env) env = pad_action_space_v0(env) env = _PettingZooEnv(env) 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 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}"
base_obs_space = {"a_{}".format(idx): Box(low=np.float32(0.0), high=np.float32(10.0), shape=[8, 8, 3]) for idx in range(2)} base_act_spaces = {"a_{}".format(idx): Discrete(5) for idx in range(2)} return PettingzooWrap(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.pad_observations_v0(new_dummy()), supersuit.pad_action_space_v0(new_dummy()), supersuit.agent_indicator_v0(new_dummy(), True), supersuit.agent_indicator_v0(new_dummy(), False), 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.sticky_actions_v0(new_dummy(), 0.75), supersuit.delay_observations_v0(new_dummy(), 3), ] @pytest.mark.parametrize("env", wrappers) def test_basic_wrappers(env): env.seed(5)
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()), 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 create_env(args): if args[env_name] == "simple_speaker_listener": env = simple_speaker_listener_v3.env() env = supersuit.pad_action_space_v0(env) env = supersuit.pad_observations_v0(env) return env
def create_env(args): env = simple_speaker_listener_v3.env() env = pad_action_space_v0(env) env = pad_observations_v0(env) return env
def env(**kwargs): env = base_env.parallel_env(**kwargs) env = pad_observations_v0(env) env = pad_action_space_v0(env) env = _ParallelPettingZooEnv(env) return env
0.75), supersuit.delay_observations_v0( generated_agents_parallel_v0.parallel_env(), 3), supersuit.max_observation_v0(generated_agents_parallel_v0.parallel_env(), 3), ] @pytest.mark.parametrize("env", parallel_wrappers) def test_pettingzoo_parallel_api_gen(env): parallel_test.parallel_api_test(env, num_cycles=50) wrapper_fns = [ lambda: supersuit.pad_action_space_v0(generated_agents_parallel_v0.env()), lambda: supersuit.pad_observations_v0(generated_agents_parallel_v0.env()), lambda: supersuit.agent_indicator_v0(generated_agents_parallel_v0.env()), lambda: supersuit.vectorize_aec_env_v0(generated_agents_parallel_v0.env(), 2), lambda: supersuit.pad_action_space_v0(generated_agents_parallel_v0. parallel_env()), lambda: supersuit.pad_observations_v0(generated_agents_parallel_v0. parallel_env()), lambda: supersuit.agent_indicator_v0(generated_agents_parallel_v0. parallel_env()), lambda: supersuit.pettingzoo_env_to_vec_env_v1(generated_agents_parallel_v0 .parallel_env()), ] @pytest.mark.parametrize("wrapper_fn", wrapper_fns)
dtype_v0(knights_archers_zombies_v10.env(vector_state=False), np.uint8), x_size=5, y_size=10, linear_interp=True, ), supersuit.dtype_v0(knights_archers_zombies_v10.env(), np.int32), 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),