def test_default_sampler_cls(self):
policy = DummyPolicy(env_spec=self.env.spec)
algo = DummyAlgo(policy=policy, baseline=self.baseline)
sampler = algo.sampler_cls(algo, self.env, dict())
assert isinstance(sampler, OnPolicyVectorizedSampler)
policy = DummyPolicyWithoutVectorized(env_spec=self.env.spec)
algo = DummyAlgo(policy=policy, baseline=self.baseline)
sampler = algo.sampler_cls(algo, self.env, dict())
assert isinstance(sampler, BatchSampler)
def test_rl2_worker(self):
env = TfEnv(DummyBoxEnv(obs_dim=(1, )))
policy = DummyPolicy(env_spec=env.spec)
worker = RL2Worker(seed=1,
max_path_length=100,
worker_number=1,
n_paths_per_trial=5)
worker.update_agent(policy)
worker.update_env(env)
rollouts = worker.rollout()
assert rollouts.rewards.shape[0] == 500
def run_task(*_):
env = normalize(CartpoleEnv())
policy = DummyPolicy(env_spec=env)
baseline = LinearFeatureBaseline(env_spec=env)
algo = InstrumentedNOP(env=env,
policy=policy,
baseline=baseline,
batch_size=4000,
max_path_length=100,
n_itr=4,
discount=0.99,
step_size=0.01,
plot=True)
algo.train()
def test_algo_with_goal_without_es(self):
# This tests if sampler works properly when algorithm
# includes goal but is without exploration policy
env = DummyDictEnv()
policy = DummyPolicy(env)
replay_buffer = SimpleReplayBuffer(env_spec=env,
size_in_transitions=int(1e6),
time_horizon=100)
algo = DummyOffPolicyAlgo(env_spec=env,
qf=None,
replay_buffer=replay_buffer,
policy=policy,
exploration_strategy=None)
sampler = OffPolicyVectorizedSampler(algo, env, 1, no_reset=True)
sampler.start_worker()
sampler.obtain_samples(0, 30)
def setup_method(self):
self.env = GarageEnv(DummyBoxEnv(obs_dim=(4, 4), action_dim=(2, 2)))
self.policy = DummyPolicy(self.env.spec)