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 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):
# 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_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),
]
@pytest.mark.parametrize("env", wrappers)
def test_basic_wrappers(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()),
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)),
def env_creator(args):
env = game_env.env()
env = flatten_v0(env)
return env
def env_creator(args):
env = game_env.env()
if env_name == 'curriculum':
env = cyclically_expansive_learning(env, [(0,1*8), (10000000,2*8), (30000000,3*8), (50000000,8*8)])
env = flatten_v0(env)
return env
import numpy as np
from pettingzoo.test import api_test, seed_test, parallel_test
from pettingzoo.test.example_envs import (
generated_agents_parallel_v0,
generated_agents_env_v0,
)
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),
]
