def test_random_actor(self):
"""Test."""
env = gym.make("LunarLanderContinuous-v2")
env = ensure_vec_env(env)
actor = RandomActor(env.action_space)
env = ResidualWrapper(env, actor)
ob, ac = env.reset()
assert ac.shape == (1, *env.action_space.shape)
assert ob.shape == (1, *env.observation_space.spaces[0].shape)
assert ac.shape == (1, *env.observation_space.spaces[1].shape)
assert isinstance(env.observation_space, Tuple)
for _ in range(10):
residual_ac = [env.action_space.sample()]
(ob, ac_next), _, _, infos = env.step(residual_ac)
rac = np.minimum(np.maximum(residual_ac[0] + ac[0], -1), 1)
assert np.allclose(infos[0]['action'], rac)
ac = ac_next
self.t = time.time()
self.env.render()
action = self._get_human_action()
return action
def reset(self):
self.human_agent_action[:] = 0.
if __name__ == '__main__':
import gym
import residual_shared_autonomy.drone_sim
from dl.rl import ensure_vec_env
env = gym.make("DroneReacherHuman-v0")
env = ensure_vec_env(env)
actor = DroneJoystickActor(env)
for _ in range(10):
ob = env.reset()
actor.reset()
env.render()
done = False
reward = 0.0
while not done:
ob, r, done, _ = env.step(actor(ob))
reward += r
print(reward)