def bc_point(ctxt=None, loss='log_prob'):
"""Run Behavioral Cloning on garage.envs.PointEnv.
Args:
ctxt (ExperimentContext): Provided by wrap_experiment.
loss (str): Either 'log_prob' or 'mse'
"""
trainer = Trainer(ctxt)
goal = np.array([1., 1.])
env = PointEnv(goal=goal, max_episode_length=200)
expert = OptimalPolicy(env.spec, goal=goal)
policy = GaussianMLPPolicy(env.spec, [8, 8])
batch_size = 1000
sampler = RaySampler(agents=expert,
envs=env,
max_episode_length=env.spec.max_episode_length)
algo = BC(env.spec,
policy,
batch_size=batch_size,
source=expert,
sampler=sampler,
policy_lr=1e-2,
loss=loss)
trainer.setup(algo, env)
trainer.train(100, batch_size=batch_size)
def test_bc_point_sample_batches():
deterministic.set_seed(100)
runner = LocalRunner(snapshot_config)
goal = np.array([1., 1.])
env = PointEnv(goal=goal)
max_episode_length = 100
source = list(expert_source(env, goal, max_episode_length, 5))
policy = DeterministicMLPPolicy(env.spec, hidden_sizes=[8, 8])
batch_size = 600
algo = BC(env.spec,
policy,
batch_size=batch_size,
source=source,
policy_lr=1e-2,
loss='mse')
runner.setup(algo, env)
run_bc(runner, algo, batch_size)
def test_bc_point_deterministic(ray_local_session_fixture): # NOQA
del ray_local_session_fixture
assert ray.is_initialized()
deterministic.set_seed(100)
runner = LocalRunner(snapshot_config)
goal = np.array([1., 1.])
env = PointEnv(goal=goal, max_episode_length=200)
expert = OptimalPolicy(env.spec, goal=goal)
policy = DeterministicMLPPolicy(env.spec, hidden_sizes=[8, 8])
batch_size = 600
algo = BC(env.spec,
policy,
batch_size=batch_size,
source=expert,
policy_lr=1e-2,
loss='mse')
runner.setup(algo, env)
run_bc(runner, algo, batch_size)
def test_bc_point(ray_local_session_fixture): # NOQA
del ray_local_session_fixture
assert ray.is_initialized()
deterministic.set_seed(100)
trainer = Trainer(snapshot_config)
goal = np.array([1., 1.])
env = PointEnv(goal=goal, max_episode_length=200)
expert = OptimalPolicy(env.spec, goal=goal)
policy = GaussianMLPPolicy(env.spec, [4])
batch_size = 400
algo = BC(env.spec,
policy,
batch_size=batch_size,
source=expert,
policy_lr=1e-2,
loss='log_prob')
trainer.setup(algo, env)
run_bc(trainer, algo, batch_size)
def bc_point(ctxt=None):
"""Run Behavioral Cloning on garage.envs.PointEnv.
Args:
ctxt (ExperimentContext): Provided by wrap_experiment.
"""
runner = LocalRunner(ctxt)
goal = np.array([1., 1.])
env = PointEnv(goal=goal, max_episode_length=200)
expert = OptimalPolicy(env.spec, goal=goal)
policy = DeterministicMLPPolicy(env.spec, hidden_sizes=[8, 8])
batch_size = 1000
algo = BC(env.spec,
policy,
batch_size=batch_size,
source=expert,
policy_lr=1e-2,
loss='mse')
runner.setup(algo, env)
runner.train(100, batch_size=batch_size)
def bc_point(ctxt=None, loss='log_prob'):
"""Run Behavioral Cloning on garage.envs.PointEnv.
Args:
ctxt (ExperimentContext): Provided by wrap_experiment.
loss (str): Either 'log_prob' or 'mse'
"""
runner = LocalRunner(ctxt)
goal = np.array([1., 1.])
env = GarageEnv(PointEnv(goal=goal))
expert = OptimalPolicy(env.spec, goal=goal)
policy = GaussianMLPPolicy(env.spec, [8, 8])
batch_size = 1000
algo = BC(env.spec,
policy,
batch_size=batch_size,
source=expert,
max_path_length=200,
policy_lr=1e-2,
loss=loss)
runner.setup(algo, env)
runner.train(100, batch_size=batch_size)