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_count=1, device="cpu"):
self.gravity = 9.8
self.masscart = 1.0
self.masspole = 0.1
self.total_mass = self.masspole + self.masscart
self.length = 0.5 # actually half the pole's length
self.polemass_length = self.masspole * self.length
self.force_mag = 10.0
self.tau = 0.02 # seconds between state updates
self.kinematics_integrator = "euler"
# Angle at which to fail the episode
self.theta_threshold_radians = 12 * 2 * math.pi / 360
self.x_threshold = 2.4
self.env_count = env_count
self.num_envs = env_count
# Angle limit set to 2 * theta_threshold_radians so failing observation
# is still within bounds.
high = np.array(
[
self.x_threshold * 2,
np.finfo(np.float32).max,
self.theta_threshold_radians * 2,
np.finfo(np.float32).max,
],
dtype=np.float32,
)
self.action_space = spaces.Discrete(2)
self.observation_space = spaces.Box(-high, high, dtype=np.float32)
self.seed()
self.viewer = None
self.state: Optional[th.Tensor] = None
self.done = th.full([env_count], True, dtype=th.bool, device=device)
self.state = th.zeros([self.env_count, 4], dtype=th.float32, device=device)
self.device = device
VecEnv.__init__(self, env_count, self.observation_space, self.action_space)
obs_space = self.observation_space
self.buf_obs = torch.zeros((1,) + (self.num_envs,) + tuple(obs_space.shape)).to(
device
)
self.buf_dones = torch.zeros((self.num_envs,), dtype=torch.bool).to(device)
self.buf_rews = torch.zeros((self.num_envs,), dtype=torch.float32).to(device)
self.buf_infos = [{} for _ in range(self.num_envs)]
self.actions: torch.FloatTensor = None
self.logged_error = False
self.envs = [self]
