def test_rendering(self):
bench = CMAESBenchmark()
env = bench.get_environment()
wrapped = StateTrackingWrapper(env)
wrapped.reset()
with pytest.raises(NotImplementedError):
wrapped.render_state_tracking()
bench = CMAESBenchmark()
def dummy():
return [1, [2, 3]]
bench.config.state_method = dummy
bench.config.observation_space = gym.spaces.Tuple(
(
gym.spaces.Discrete(2),
gym.spaces.Box(low=np.array([-1, 1]), high=np.array([5, 5])),
)
)
env = bench.get_environment()
wrapped = StateTrackingWrapper(env)
wrapped.reset()
with pytest.raises(NotImplementedError):
wrapped.render_state_tracking()
def dummy2():
return [0.5]
bench.config.state_method = dummy2
bench.config.observation_space = gym.spaces.Box(
low=np.array([0]), high=np.array([1])
)
env = bench.get_environment()
wrapped = StateTrackingWrapper(env)
wrapped.reset()
wrapped.step(1)
wrapped.step(1)
img = wrapped.render_state_tracking()
self.assertTrue(img.shape[-1] == 3)
bench = LubyBenchmark()
env = bench.get_environment()
wrapped = StateTrackingWrapper(env, 2)
wrapped.reset()
wrapped.step(1)
wrapped.step(1)
img = wrapped.render_state_tracking()
self.assertTrue(img.shape[-1] == 3)
class discrete_obs_env:
def __init__(self):
self.observation_space = gym.spaces.Discrete(2)
self.action_space = gym.spaces.Discrete(2)
self.reward_range = (1, 2)
self.metadata = {}
def reset(self):
return 1
def step(self, action):
return 1, 1, 1, 1
env = discrete_obs_env()
wrapped = StateTrackingWrapper(env, 2)
wrapped.reset()
wrapped.step(1)
img = wrapped.render_state_tracking()
self.assertTrue(img.shape[-1] == 3)
class multi_discrete_obs_env:
def __init__(self):
self.observation_space = gym.spaces.MultiDiscrete([2, 3])
self.action_space = gym.spaces.Discrete(2)
self.reward_range = (1, 2)
self.metadata = {}
def reset(self):
return [1, 2]
def step(self, action):
return [1, 2], 1, 1, 1
env = multi_discrete_obs_env()
wrapped = StateTrackingWrapper(env)
wrapped.reset()
wrapped.step(1)
img = wrapped.render_state_tracking()
self.assertTrue(img.shape[-1] == 3)
class multi_binary_obs_env:
def __init__(self):
self.observation_space = gym.spaces.MultiBinary(2)
self.action_space = gym.spaces.Discrete(2)
self.reward_range = (1, 2)
self.metadata = {}
def reset(self):
return [1, 1]
def step(self, action):
return [1, 1], 1, 1, 1
env = multi_binary_obs_env()
wrapped = StateTrackingWrapper(env)
wrapped.reset()
wrapped.step(1)
img = wrapped.render_state_tracking()
self.assertTrue(img.shape[-1] == 3)
from chainerrl import wrappers import matplotlib.pyplot as plt from examples.example_utils import train_chainer, make_chainer_dqn from dacbench.benchmarks import FastDownwardBenchmark from dacbench.wrappers import StateTrackingWrapper # Get FastDownward Environment bench = FastDownwardBenchmark() env = bench.get_environment() # Wrap environment to track state # In this case we also want the mean of each 5 step interval env = StateTrackingWrapper(env, 5) # Chainer requires casting to float32 env = wrappers.CastObservationToFloat32(env) # Make chainer agent obs_size = env.observation_space.low.size agent = make_chainer_dqn(obs_size, env.action_space) # Train for 10 episodes train_chainer(agent, env) # Plot state values after training env.render_state_tracking() plt.show()