def __init__(self,
env_fns: List[Tuple[Callable[[], Union[str, gym.Env]],
Callable[[torch.Tensor],
Union[torch.Tensor, np.ndarray,
list]]]]):
""" Constructor
Args:
env_fns: a list of pairs of functions, the first of which create one environment and the second is the mapper
that converts the action outputs from the model to what can be accepted by the env object.
See VectorizedMultiAgentEnvWrapper.MultiAgentEnvWrapper for details
"""
self.envs = [
self.MultiAgentEnvWrapper(fn_pair[0](), fn_pair[1])
for fn_pair in env_fns
]
env = self.envs[0]
VecEnv.__init__(self, len(env_fns), env.observation_space,
env.action_space)
obs_space = env.observation_space
self.keys, shapes, dtypes = obs_space_info(obs_space)
self.buf_obs = OrderedDict([(k,
np.zeros(
(self.num_envs, ) + tuple(shapes[k]),
dtype=dtypes[k])) for k in self.keys])
self.buf_dones = np.zeros((self.num_envs, 1), dtype=bool)
self.buf_rews = np.zeros((self.num_envs, 1, env.agent_num),
dtype=np.float32)
self.buf_infos = [{} for _ in range(self.num_envs)]
self.actions = None
self.metadata = env.metadata
def __init__(self):
# self.stackSize = 10
self.scanSize = 1080
self.action_space = spaces.Box(low=np.array([-1, -1]),
high=np.array([1, 1]),
dtype=np.float32)
# stacked self.stackSize latest observations of (scan, x,y,theta,vel_x, vel_y, vel_z)*2 (we get the data from the opponent as well)
self.observation_space = spaces.Box(low=0.0,
high=1.0,
shape=((self.scanSize + 6) * 2, ),
dtype=np.float32)
VecEnv.__init__(self, 2, self.observation_space, self.action_space)
self.keys, shapes, dtypes = obs_space_info(self.observation_space)
self.actions = None
# simualtor params
self.params_set = False
self.map_inited = False
# params list is [mu, h_cg, l_r, cs_f, cs_r, I_z, mass]
self.params = []
self.num_agents = 2
self.timestep = 0.01
self.map_path = None
self.map_img = None
self.ego_idx = 0
self.timeout = 120.0
# radius to consider done
self.start_thresh = 0.5 # 10cm
# env states
# more accurate description should be ego car state
# might not need to keep scan
self.x = None
self.y = None
self.theta = None
self.in_collision = False
self.collision_angle = None
# loop completion
self.near_start = True
self.num_toggles = 0
# race info
self.lap_times = [0.0, 0.0]
self.lap_counts = [0, 0]
self.map_height = 0.0
self.map_width = 0.0
self.map_resolution = 0.0
self.free_thresh = 0.0
self.origin = []
self.port = 6666
self.context = zmq.Context()
self.socket = self.context.socket(zmq.PAIR)
self.socket.connect('tcp://localhost:6666')
print('Gym env - Connected env to port: ' + str(self.port))
self.sim_p = None
print('Gym env - env created, waiting for params...')
def __init__(self, env_fns: List[Callable[[], gym.Env]]):
self.envs = [fn() for fn in env_fns]
env = self.envs[0]
VecEnv.__init__(self, len(env_fns), env.observation_space, env.action_space)
obs_space = env.observation_space
self.keys, shapes, dtypes = obs_space_info(obs_space)
self.buf_obs = OrderedDict([(k, np.zeros((self.num_envs,) + tuple(shapes[k]), dtype=dtypes[k])) for k in self.keys])
self.buf_dones = np.zeros((self.num_envs,), dtype=np.bool)
self.buf_rews = np.zeros((self.num_envs,), dtype=np.float32)
self.buf_infos = [{} for _ in range(self.num_envs)]
self.actions = None
self.metadata = env.metadata