def test_get_actions(batch_size):
env_spec = GymEnv(DummyBoxEnv()).spec
obs_dim = env_spec.observation_space.flat_dim
obs = torch.ones([batch_size, obs_dim], dtype=torch.float32)
qf = DiscreteMLPQFunction(env_spec=env_spec,
hidden_nonlinearity=None,
hidden_sizes=(2, 2))
qvals = qf(obs)
policy = DiscreteQFArgmaxPolicy(qf, env_spec)
actions, _ = policy.get_actions(obs.numpy())
assert (actions == torch.argmax(qvals, dim=1).numpy()).all()
assert actions.shape == (batch_size, )
def test_get_action():
env_spec = GymEnv(DummyBoxEnv()).spec
obs_dim = env_spec.observation_space.flat_dim
obs = torch.ones([
obs_dim,
], dtype=torch.float32)
qf = DiscreteMLPQFunction(env_spec=env_spec,
hidden_nonlinearity=None,
hidden_sizes=(2, 2))
qvals = qf(obs.unsqueeze(0))
policy = DiscreteQFArgmaxPolicy(qf, env_spec)
action, _ = policy.get_action(obs.numpy())
assert action == torch.argmax(qvals, dim=1).numpy()
assert action.shape == ()
def test_is_pickleable(batch_size):
env_spec = GymEnv(DummyBoxEnv())
obs_dim = env_spec.observation_space.flat_dim
obs = torch.ones([batch_size, obs_dim], dtype=torch.float32)
qf = DiscreteMLPQFunction(env_spec=env_spec,
hidden_nonlinearity=None,
hidden_sizes=(2, 2))
policy = DiscreteQFArgmaxPolicy(qf, env_spec)
output1 = policy.get_actions(obs.numpy())[0]
p = pickle.dumps(policy)
policy_pickled = pickle.loads(p)
output2 = policy_pickled.get_actions(obs.numpy())[0]
assert np.array_equal(output1, output2)
def setup():
set_seed(24)
n_epochs = 11
steps_per_epoch = 10
sampler_batch_size = 512
num_timesteps = 100 * steps_per_epoch * sampler_batch_size
env = GymEnv('CartPole-v0')
replay_buffer = PathBuffer(capacity_in_transitions=int(1e6))
qf = DiscreteMLPQFunction(env_spec=env.spec, hidden_sizes=(8, 5))
policy = DiscreteQFArgmaxPolicy(env_spec=env.spec, qf=qf)
exploration_policy = EpsilonGreedyPolicy(env_spec=env.spec,
policy=policy,
total_timesteps=num_timesteps,
max_epsilon=1.0,
min_epsilon=0.01,
decay_ratio=0.4)
algo = DQN(env_spec=env.spec,
policy=policy,
qf=qf,
exploration_policy=exploration_policy,
replay_buffer=replay_buffer,
steps_per_epoch=steps_per_epoch,
qf_lr=5e-5,
discount=0.9,
min_buffer_size=int(1e4),
n_train_steps=500,
target_update_freq=30,
buffer_batch_size=64)
return algo, env, replay_buffer, n_epochs, sampler_batch_size
def dqn_cartpole(ctxt=None, seed=24):
"""Train DQN with CartPole-v0 environment.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by LocalRunner to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
"""
set_seed(seed)
runner = Trainer(ctxt)
n_epochs = 100
steps_per_epoch = 10
sampler_batch_size = 512
num_timesteps = n_epochs * steps_per_epoch * sampler_batch_size
env = GymEnv('CartPole-v0')
replay_buffer = PathBuffer(capacity_in_transitions=int(1e6))
qf = DiscreteMLPQFunction(env_spec=env.spec, hidden_sizes=(8, 5))
policy = DiscreteQFArgmaxPolicy(env_spec=env.spec, qf=qf)
exploration_policy = EpsilonGreedyPolicy(env_spec=env.spec,
policy=policy,
total_timesteps=num_timesteps,
max_epsilon=1.0,
min_epsilon=0.01,
decay_ratio=0.4)
sampler = LocalSampler(agents=exploration_policy,
envs=env,
max_episode_length=env.spec.max_episode_length,
worker_class=FragmentWorker)
algo = DQN(env_spec=env.spec,
policy=policy,
qf=qf,
exploration_policy=exploration_policy,
replay_buffer=replay_buffer,
sampler=sampler,
steps_per_epoch=steps_per_epoch,
qf_lr=5e-5,
discount=0.9,
min_buffer_size=int(1e4),
n_train_steps=500,
target_update_freq=30,
buffer_batch_size=64)
runner.setup(algo, env)
runner.train(n_epochs=n_epochs, batch_size=sampler_batch_size)
env.close()
def train_dqn(ctxt=None):
set_seed(seed)
trainer = Trainer(ctxt)
env = MyGymEnv(gym_env, max_episode_length=100)
steps_per_epoch = 10
sampler_batch_size = 4000
num_timesteps = n_eps * steps_per_epoch * sampler_batch_size
replay_buffer = PathBuffer(capacity_in_transitions=int(1e6))
qf = DiscreteMLPQFunction(env_spec=env.spec, hidden_sizes=(8, 5))
policy = DiscreteQFArgmaxPolicy(env_spec=env.spec, qf=qf)
exploration_policy = EpsilonGreedyPolicy(
env_spec=env.spec,
policy=policy,
total_timesteps=num_timesteps,
max_epsilon=1.0,
min_epsilon=0.01,
decay_ratio=0.4,
)
sampler = LocalSampler(
agents=exploration_policy,
envs=env,
max_episode_length=env.spec.max_episode_length,
worker_class=FragmentWorker,
)
self.algo = LoggedDQN(
env=env,
env_spec=env.spec,
policy=policy,
qf=qf,
exploration_policy=exploration_policy,
replay_buffer=replay_buffer,
sampler=sampler,
steps_per_epoch=steps_per_epoch,
qf_lr=5e-5,
discount=0.99,
min_buffer_size=int(1e4),
n_train_steps=500,
target_update_freq=30,
buffer_batch_size=64,
)
trainer.setup(self.algo, env)
trainer.train(n_epochs=n_eps, batch_size=sampler_batch_size)
return self.algo.rew_chkpts
def dqn_atari(ctxt=None,
env=None,
seed=24,
n_workers=psutil.cpu_count(logical=False),
max_episode_length=None,
**kwargs):
"""Train DQN with PongNoFrameskip-v4 environment.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
env (str): Name of the atari environment, eg. 'PongNoFrameskip-v4'.
seed (int): Used to seed the random number generator to produce
determinism.
n_workers (int): Number of workers to use. Defaults to the number of
CPU cores available.
max_episode_length (int): Max length of an episode. If None, defaults
to the timelimit specific to the environment. Used by integration
tests.
kwargs (dict): hyperparameters to be saved to variant.json.
"""
assert n_workers > 0
assert env is not None
env = gym.make(env)
env = Noop(env, noop_max=30)
env = MaxAndSkip(env, skip=4)
env = EpisodicLife(env)
if 'FIRE' in env.unwrapped.get_action_meanings():
env = FireReset(env)
env = Grayscale(env)
env = Resize(env, 84, 84)
env = ClipReward(env)
env = StackFrames(env, 4, axis=0)
env = GymEnv(env, max_episode_length=max_episode_length, is_image=True)
set_seed(seed)
trainer = Trainer(ctxt)
n_epochs = hyperparams['n_epochs']
steps_per_epoch = hyperparams['steps_per_epoch']
sampler_batch_size = hyperparams['sampler_batch_size']
num_timesteps = n_epochs * steps_per_epoch * sampler_batch_size
replay_buffer = PathBuffer(
capacity_in_transitions=hyperparams['buffer_size'])
qf = DiscreteCNNQFunction(
env_spec=env.spec,
image_format='NCHW',
hidden_channels=hyperparams['hidden_channels'],
kernel_sizes=hyperparams['kernel_sizes'],
strides=hyperparams['strides'],
hidden_w_init=(
lambda x: torch.nn.init.orthogonal_(x, gain=np.sqrt(2))),
hidden_sizes=hyperparams['hidden_sizes'])
policy = DiscreteQFArgmaxPolicy(env_spec=env.spec, qf=qf)
exploration_policy = EpsilonGreedyPolicy(
env_spec=env.spec,
policy=policy,
total_timesteps=num_timesteps,
max_epsilon=hyperparams['max_epsilon'],
min_epsilon=hyperparams['min_epsilon'],
decay_ratio=hyperparams['decay_ratio'])
sampler = LocalSampler(agents=exploration_policy,
envs=env,
max_episode_length=env.spec.max_episode_length,
worker_class=FragmentWorker,
n_workers=n_workers)
algo = DQN(env_spec=env.spec,
policy=policy,
qf=qf,
exploration_policy=exploration_policy,
replay_buffer=replay_buffer,
sampler=sampler,
steps_per_epoch=steps_per_epoch,
qf_lr=hyperparams['lr'],
clip_gradient=hyperparams['clip_gradient'],
discount=hyperparams['discount'],
min_buffer_size=hyperparams['min_buffer_size'],
n_train_steps=hyperparams['n_train_steps'],
target_update_freq=hyperparams['target_update_freq'],
buffer_batch_size=hyperparams['buffer_batch_size'])
set_gpu_mode(False)
torch.set_num_threads(1)
if torch.cuda.is_available():
set_gpu_mode(True)
algo.to()
trainer.setup(algo, env)
trainer.train(n_epochs=n_epochs, batch_size=sampler_batch_size)
env.close()
