def test_reward(environment, action_cost, action_type):
env_name, reward_model_ = environment
if action_cost is not None:
env = GymEnvironment(env_name, action_cost=action_cost)
else:
env = GymEnvironment(env_name)
state = env.reset()
if action_cost is not None:
reward_model = reward_model_(action_cost=action_cost)
else:
reward_model = reward_model_()
reward_model.set_goal(env.goal)
for _ in range(50):
if action_type == "random":
action = env.action_space.sample()
elif action_type == "zero":
action = np.zeros(env.dim_action)
else:
raise NotImplementedError
next_state, reward, done, info = env.step(action)
if env.goal is not None:
state = np.concatenate((state, env.goal))
np.testing.assert_allclose(reward,
reward_model(state, action, next_state)[0],
rtol=1e-3,
atol=1e-6)
np.testing.assert_allclose(
np.tile(reward, (5, )),
reward_model(
np.tile(state, (5, 1)),
np.tile(action, (5, 1)),
np.tile(next_state, (5, 1)),
)[0],
rtol=1e-3,
atol=1e-6,
)
state = torch.tensor(state, dtype=torch.get_default_dtype())
action = torch.tensor(action, dtype=torch.get_default_dtype())
next_state = torch.tensor(next_state, dtype=torch.get_default_dtype())
np.testing.assert_allclose(reward,
reward_model(state, action, next_state)[0],
rtol=1e-3,
atol=1e-6)
np.testing.assert_allclose(
np.tile(reward, (5, 1)),
reward_model(state.repeat(5, 1), action.repeat(5, 1),
next_state.repeat(5, 1))[0],
rtol=1e-3,
atol=1e-6,
)
state = next_state.numpy()
def test_set_state_np(self):
env = GymEnvironment("VPendulum-v0")
env.reset()
state, action = self.state_action
action = np.round(action)
env.state = state
obs, _, _, _ = env.step(action)
state = env.state
np.testing.assert_allclose(obs, state)
def test_tolerance(action_cost):
env_name, reward_model_ = ("MBRLReacher3D-v0", ReacherReward)
if action_cost is not None:
env = GymEnvironment(env_name, action_cost=action_cost, sparse=True)
else:
env = GymEnvironment(env_name, sparse=True)
state = env.reset()
if action_cost is not None:
reward_model = reward_model_(action_cost=action_cost, sparse=True)
else:
reward_model = reward_model_(sparse=True)
reward_model.set_goal(env.goal)
for _ in range(50):
action = env.action_space.sample()
next_state, reward, done, info = env.step(action)
if env.goal is not None:
state = np.concatenate((state, env.goal))
np.testing.assert_allclose(reward,
reward_model(state, action, next_state)[0],
rtol=1e-3,
atol=1e-6)
np.testing.assert_allclose(
np.tile(reward, (5, )),
reward_model(
np.tile(state, (5, 1)),
np.tile(action, (5, 1)),
np.tile(next_state, (5, 1)),
)[0],
rtol=1e-3,
atol=1e-6,
)
state = torch.tensor(state, dtype=torch.get_default_dtype())
action = torch.tensor(action, dtype=torch.get_default_dtype())
next_state = torch.tensor(next_state, dtype=torch.get_default_dtype())
np.testing.assert_allclose(reward,
reward_model(state, action, next_state)[0],
rtol=1e-3,
atol=1e-6)
np.testing.assert_allclose(
np.tile(reward, (5, 1)),
reward_model(state.repeat(5, 1), action.repeat(5, 1),
next_state.repeat(5, 1))[0],
rtol=1e-3,
atol=1e-6,
)
state = next_state.numpy()
"probability": 0.5,
"reward": reward
})
for j in range(8):
for a in range(2):
transitions[(3 + j, a)].append({
"next_state": 0,
"probability": 1.0,
"reward": 0
})
return transitions
if __name__ == "__main__":
from rllib.environment import GymEnvironment
from rllib.environment.utilities import transitions2kernelreward
import qreps # noqa: F401
env = GymEnvironment("WideTree-v0", reward=1)
kernel, reward = transitions2kernelreward(env.env.transitions,
env.num_states, env.num_actions)
print(kernel, reward)
state = env.reset()
print(state)
for i in range(10):
action = env.action_space.sample()
next_state, r, done, f = env.step(action)
print(state, action, next_state, r, done)
state = next_state