def test_read_instances(self):
bench = FastDownwardBenchmark()
bench.read_instance_set()
self.assertTrue(len(bench.config.instance_set) == 30)
self.assertTrue(type(bench.config.instance_set[0]) == str)
self.assertTrue(os.path.isfile(bench.config.instance_set[0]))
path = bench.config.instance_set[0]
bench2 = FastDownwardBenchmark()
env = bench2.get_environment()
self.assertTrue(type(env.instance_set[0]) == str)
self.assertTrue(len(env.instance_set) == 30)
self.assertTrue(path == env.instance_set[0])
def test_save_conf(self):
bench = FastDownwardBenchmark()
bench.save_config("test_conf.json")
with open("test_conf.json", "r") as fp:
recovered = json.load(fp)
for k in bench.config.keys():
self.assertTrue(k in recovered.keys())
os.remove("test_conf.json")
def test_get_env(self):
bench = FastDownwardBenchmark()
env = bench.get_environment()
self.assertTrue(issubclass(type(env), FastDownwardEnv))
bench.config.instance_set_path = "../instance_sets/fast_downward/childsnack"
bench.read_instance_set()
env = bench.get_environment()
self.assertTrue(issubclass(type(env), FastDownwardEnv))
def test_scenarios(self):
scenarios = [
"fd_barman.json",
"fd_blocksworld.json",
"fd_visitall.json",
"fd_childsnack.json",
"fd_sokoban.json",
"fd_rovers.json",
]
for s in scenarios:
path = os.path.join("dacbench/scenarios/fast_downward/", s)
bench = FastDownwardBenchmark(path)
self.assertTrue(bench.config is not None)
env = bench.get_environment()
state = env.reset()
self.assertTrue(state is not None)
state, _, _, _ = env.step(0)
self.assertTrue(state is not None)
def test_rendering(self):
bench = FastDownwardBenchmark()
env = bench.get_environment()
wrapped = ActionFrequencyWrapper(env, 2)
wrapped.reset()
for _ in range(10):
wrapped.step(1)
img = wrapped.render_action_tracking()
self.assertTrue(img.shape[-1] == 3)
bench = CMAESBenchmark()
env = bench.get_environment()
wrapped = ActionFrequencyWrapper(env, 2)
wrapped.reset()
wrapped.step(np.ones(10))
img = wrapped.render_action_tracking()
self.assertTrue(img.shape[-1] == 3)
class dict_action_env:
def __init__(self):
self.action_space = gym.spaces.Dict({
"one":
gym.spaces.Discrete(2),
"two":
gym.spaces.Box(low=np.array([-1, 1]),
high=np.array([1, 5])),
})
self.observation_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 = dict_action_env()
wrapped = ActionFrequencyWrapper(env)
wrapped.reset()
with self.assertRaises(NotImplementedError):
wrapped.render_action_tracking()
class tuple_action_env:
def __init__(self):
self.action_space = gym.spaces.Tuple((
gym.spaces.Discrete(2),
gym.spaces.Box(low=np.array([-1, 1]),
high=np.array([1, 5])),
))
self.observation_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 = tuple_action_env()
wrapped = ActionFrequencyWrapper(env)
wrapped.reset()
with self.assertRaises(NotImplementedError):
wrapped.render_action_tracking()
class multi_discrete_action_env:
def __init__(self):
self.action_space = gym.spaces.MultiDiscrete([2, 3])
self.observation_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 = multi_discrete_action_env()
wrapped = ActionFrequencyWrapper(env, 5)
wrapped.reset()
for _ in range(10):
wrapped.step([1, 2])
img = wrapped.render_action_tracking()
self.assertTrue(img.shape[-1] == 3)
class multi_binary_action_env:
def __init__(self):
self.action_space = gym.spaces.MultiBinary(2)
self.observation_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 = multi_binary_action_env()
wrapped = ActionFrequencyWrapper(env)
wrapped.reset()
wrapped.step([1, 0])
img = wrapped.render_action_tracking()
self.assertTrue(img.shape[-1] == 3)
class large_action_env:
def __init__(self):
self.action_space = gym.spaces.Box(low=np.zeros(15),
high=np.ones(15))
self.observation_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 = large_action_env()
wrapped = ActionFrequencyWrapper(env)
wrapped.reset()
wrapped.step(0.5 * np.ones(15))
img = wrapped.render_action_tracking()
self.assertTrue(img.shape[-1] == 3)
def test_benchmark_env(self):
bench = FastDownwardBenchmark()
env = bench.get_benchmark()
self.assertTrue(issubclass(type(env), FastDownwardEnv))
def make_fast_downward(config):
bench = FastDownwardBenchmark()
return bench.get_benchmark(config["seed"])
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()