class FooCarEnv(gym.Env):
_channel = EnvironmentParametersChannel()
PathSpace = {
'xyz': 0,
'xy': 2,
'yz': 2,
'xz': 2
}
def __init__(self, no_graphics:bool=False, seed:int=1, **config):
self._config = config
worker_id = 0
if 'worker_id' in config:
worker_id = config['worker_id']
self._unity_env = UnityEnvironment(
file_name=UNITY_ENV_EXE_FILE,
# file_name=None, # Unity Editor Mode (debug)
no_graphics=no_graphics,
seed=seed,
side_channels=[self._channel],
worker_id=worker_id
)
for key, value in config.items():
self._channel.set_float_parameter(key, float(value))
self._gym_env = UnityToGymWrapper(self._unity_env)
def step(self, action):
obs, reward, done, info = self._gym_env.step(action)
size = self.observation_size
return obs[:size], reward, done, info
def reset(self):
obs = self._gym_env.reset()
size = self.observation_size
return obs[:size]
def render(self, mode="rgb_array"):
return self._gym_env.render(mode=mode)
def seed(self, seed=None):
self._gym_env.seed(seed=seed) # it will throw a warning
def close(self):
self._gym_env.close()
@property
def metadata(self):
return self._gym_env.metadata
@property
def reward_range(self) -> Tuple[float, float]:
return self._gym_env.reward_range
@property
def action_space(self):
return self._gym_env.action_space
@property
def observation_space(self):
config = self._config
space = self.PathSpace
path_space = config['path_space'] if 'path_space' in config else space['xz']
r = config['radius_anchor_circle'] if 'radius_anchor_circle' in config else 8.0
r_e = config['radius_epsilon_ratio'] if 'radius_epsilon_ratio' in config else 0.7
h = config['max_anchor_height'] if 'max_anchor_height' in config else 1.0
xyz_mode = (path_space == space['xyz'])
bound = max(r * (1 + r_e), h if xyz_mode else 0)
shape = (self.observation_size,)
return gym.spaces.Box(-bound, +bound, dtype=np.float32, shape=shape)
@property
def observation_size(self):
# Reference: readonly variable (Unity)FooCar/CarAgent.ObservationSize
config = self._config
space = self.PathSpace
path_space = config['path_space'] if 'path_space' in config else space['xz']
ticker_end = config['ticker_end'] if 'ticker_end' in config else 5
ticker_start = config['ticker_start'] if 'ticker_start' in config else -3
xyz_mode = (path_space == space['xyz'])
basic_num = 6
point_dim = 3 if xyz_mode else 2
return basic_num + 2 * point_dim * (ticker_end - ticker_start + 1)
torch.manual_seed(args.seed)
np.random.seed(args.seed)
engine_configuration_channel = EngineConfigurationChannel()
unity_env = UnityEnvironment(side_channels=[engine_configuration_channel],
file_name=args.env)
engine_configuration_channel.set_configuration_parameters(
width=200,
height=200,
quality_level=5,
time_scale=1 if args.show else 20,
target_frame_rate=-1,
capture_frame_rate=60)
env = UnityToGymWrapper(unity_env=unity_env)
env.seed(args.seed)
env.action_space.seed(args.seed)
train_tools.EVAL_SEED = args.seed
obs_dim = env.observation_space.shape[0]
act_dim = env.action_space.shape[0]
act_bound = env.action_space.high[0]
# create nets
actor_net = DDPGMLPActor(obs_dim=obs_dim,
act_dim=act_dim,
act_bound=act_bound,
hidden_size=[400, 300],
hidden_activation=nn.ReLU)
critic_net1 = MLPQsaNet(obs_dim=obs_dim,