def test_trpo_pendulum(self):
"""Test TRPO with Pendulum environment."""
deterministic.set_seed(0)
runner = LocalRunner(snapshot_config)
algo = TRPO(env_spec=self.env.spec,
policy=self.policy,
value_function=self.value_function,
max_path_length=100,
discount=0.99,
gae_lambda=0.98)
runner.setup(algo, self.env)
last_avg_ret = runner.train(n_epochs=10, batch_size=100)
assert last_avg_ret > 0
def run_garage(env, seed, log_dir):
'''
Create garage model and training.
Replace the ddpg with the algorithm you want to run.
:param env: Environment of the task.
:param seed: Random seed for the trial.
:param log_dir: Log dir path.
:return:
'''
deterministic.set_seed(seed)
with LocalRunner() as runner:
env = TfEnv(normalize(env))
# Set up params for ddpg
action_noise = OUStrategy(env.spec, sigma=params['sigma'])
policy = ContinuousMLPPolicyWithModel(
env_spec=env.spec,
hidden_sizes=params['policy_hidden_sizes'],
hidden_nonlinearity=tf.nn.relu,
output_nonlinearity=tf.nn.tanh)
qf = ContinuousMLPQFunction(env_spec=env.spec,
hidden_sizes=params['qf_hidden_sizes'],
hidden_nonlinearity=tf.nn.relu)
replay_buffer = SimpleReplayBuffer(
env_spec=env.spec,
size_in_transitions=params['replay_buffer_size'],
time_horizon=params['n_rollout_steps'])
ddpg = DDPG(env_spec=env.spec,
policy=policy,
qf=qf,
replay_buffer=replay_buffer,
policy_lr=params['policy_lr'],
qf_lr=params['qf_lr'],
target_update_tau=params['tau'],
n_train_steps=params['n_train_steps'],
discount=params['discount'],
min_buffer_size=int(1e4),
exploration_strategy=action_noise,
policy_optimizer=tf.train.AdamOptimizer,
qf_optimizer=tf.train.AdamOptimizer)
# Set up logger since we are not using run_experiment
tabular_log_file = osp.join(log_dir, 'progress.csv')
tensorboard_log_dir = osp.join(log_dir)
dowel_logger.add_output(dowel.StdOutput())
dowel_logger.add_output(dowel.CsvOutput(tabular_log_file))
dowel_logger.add_output(dowel.TensorBoardOutput(tensorboard_log_dir))
runner.setup(ddpg, env)
runner.train(n_epochs=params['n_epochs'],
n_epoch_cycles=params['n_epoch_cycles'],
batch_size=params['n_rollout_steps'])
dowel_logger.remove_all()
return tabular_log_file
def setup_method(self):
"""Setup method which is called before every test."""
self._env = GymEnv('InvertedDoublePendulum-v2', max_episode_length=100)
self._runner = LocalRunner(snapshot_config)
self._policy = GaussianMLPPolicy(env_spec=self._env.spec,
hidden_sizes=[64, 64],
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
self._params = {
'env_spec': self._env.spec,
'policy': self._policy,
'value_function':
GaussianMLPValueFunction(env_spec=self._env.spec),
'discount': 0.99,
}
def test_resume(self):
with LocalRunner(self.snapshot_config, self.sess) as runner:
args = runner.restore(self.temp_dir.name)
assert np.equal(
runner.policy.get_param_values(),
self.policy_params).all(), 'Policy parameters should persist'
assert args.n_epochs == 5, (
'Snapshot should save training parameters')
assert args.start_epoch == 5, (
'Last experiment should end at 5th iterations')
batch_size = runner.train_args.batch_size
n_epoch_cycles = runner.train_args.n_epoch_cycles
runner.resume(n_epochs=10,
plot=False,
store_paths=True,
pause_for_plot=False)
assert runner.train_args.n_epochs == 10
assert runner.train_args.batch_size == batch_size
assert runner.train_args.n_epoch_cycles == n_epoch_cycles
assert not runner.train_args.plot
assert runner.train_args.store_paths
assert not runner.train_args.pause_for_plot
def test_ppo_pendulum_recurrent(self):
"""Test PPO with Pendulum environment and recurrent policy."""
with LocalRunner() as runner:
logger.reset()
env = TfEnv(normalize(gym.make("InvertedDoublePendulum-v2")))
policy = GaussianLSTMPolicy(env_spec=env.spec, )
baseline = GaussianMLPBaseline(
env_spec=env.spec,
regressor_args=dict(hidden_sizes=(32, 32)),
)
algo = PPO(
env=env,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
lr_clip_range=0.01,
optimizer_args=dict(batch_size=32, max_epochs=10),
plot=False,
)
runner.setup(algo, env)
last_avg_ret = runner.train(n_epochs=10, batch_size=2048)
assert last_avg_ret > 40
env.close()
def test_cem_cartpole(self):
"""Test CEM with Cartpole-v1 environment."""
with LocalRunner() as runner:
env = TfEnv(env_name="CartPole-v1")
policy = CategoricalMLPPolicy(
name="policy", env_spec=env.spec, hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
runner.initialize_tf_vars()
n_samples = 10
algo = CEM(
env_spec=env.spec,
policy=policy,
baseline=baseline,
best_frac=0.1,
max_path_length=100,
n_samples=n_samples)
runner.setup(algo, env, sampler_cls=OnPolicyVectorizedSampler)
rtn = runner.train(
n_epochs=5, batch_size=2000, n_epoch_cycles=n_samples)
assert rtn > 40
env.close()
def run_task(*_):
with LocalRunner() as runner:
env = TfEnv(
normalize(
OneHotMultiTaskEnv(task_env_cls=PointEnv,
task_args=TASK_ARGS,
task_kwargs=TASK_KWARGS)))
policy = GaussianMLPPolicy(name="policy",
env_spec=env.spec,
hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
max_kl_step=0.01,
)
runner.setup(algo, env)
runner.train(n_epochs=100, batch_size=4000)
def run_task(v):
v = SimpleNamespace(**v)
with LocalRunner() as runner:
# Environment
env = SimpleReacherEnv(goal_position=GOALS[0],
control_method="position_control",
completion_bonus=5)
env = TfEnv(env)
# Policy
policy = GaussianMLPPolicy(
name="policy",
env_spec=env.spec,
hidden_sizes=(64, 32),
init_std=v.policy_init_std,
)
baseline = GaussianMLPBaseline(env_spec=env.spec)
algo = PPO(
env=env,
policy=policy,
baseline=baseline,
max_path_length=v.max_path_length,
discount=0.99,
lr_clip_range=0.2,
optimizer_args=dict(batch_size=32, max_epochs=10),
plot=True,
)
runner.setup(algo, env)
runner.train(n_epochs=1000, batch_size=v.batch_size, plot=False)
def run_task(*_):
sess = tf.Session()
sess.__enter__()
latent_policy = joblib.load(latent_policy_pkl)["policy"]
with LocalRunner(sess=sess) as runner:
inner_env = PointEnv(goal=(1.4, 1.4), completion_bonus=100)
env = TfEnv(EmbeddedPolicyEnv(inner_env, latent_policy))
policy = GaussianMLPPolicy(name="composer",
env_spec=env.spec,
hidden_sizes=(64, 64),
init_std=20,
std_share_network=False,
adaptive_std=True)
baseline = GaussianMLPBaseline(env_spec=env)
algo = PPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=1024, # 4096
max_path_length=50,
n_itr=1500,
discount=0.99,
step_size=0.2,
policy_ent_coeff=1e-6,
plot=True,
use_mpc_es=True,
)
runner.setup(algo, env)
runner.train(n_epochs=600, plot=False, batch_size=1024)
def run_task(*_):
with LocalRunner() as runner:
env = TfEnv(normalize(PointEnv(goal=(-1, 0))))
policy = GaussianMLPPolicy(name="policy",
env_spec=env.spec,
hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
max_kl_step=0.01,
)
batch_size = 4000
max_path_length = 100
n_envs = batch_size // max_path_length
runner.setup(algo, env)
runner.train(n_epochs=100, batch_size=batch_size, plot=False)
def run_task(*_):
"""Run the job."""
with LocalRunner() as runner:
env = TfEnv(normalize(gym.make('InvertedPendulum-v2')))
policy = GaussianMLPPolicy(env_spec=env.spec, hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
optimizer_args = dict(
# debug_nan=True,
# reg_coeff=0.1,
# cg_iters=2
)
algo = TRPO(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
max_kl_step=0.01,
optimizer_args=optimizer_args)
runner.setup(algo,
env,
sampler_cls=ISSampler,
sampler_args=dict(n_backtrack=1))
runner.train(n_epochs=200, batch_size=4000)
def test_ddpg_pendulum(self):
"""Test DDPG with Pendulum environment."""
with LocalRunner(self.sess) as runner:
env = TfEnv(gym.make('InvertedDoublePendulum-v2'))
action_noise = OUStrategy(env.spec, sigma=0.2)
policy = ContinuousMLPPolicy(env_spec=env.spec,
hidden_sizes=[64, 64],
hidden_nonlinearity=tf.nn.relu,
output_nonlinearity=tf.nn.tanh)
qf = ContinuousMLPQFunction(env_spec=env.spec,
hidden_sizes=[64, 64],
hidden_nonlinearity=tf.nn.relu)
replay_buffer = SimpleReplayBuffer(env_spec=env.spec,
size_in_transitions=int(1e6),
time_horizon=100)
algo = DDPG(
env_spec=env.spec,
policy=policy,
policy_lr=1e-4,
qf_lr=1e-3,
qf=qf,
replay_buffer=replay_buffer,
target_update_tau=1e-2,
n_train_steps=50,
discount=0.9,
min_buffer_size=int(1e4),
exploration_strategy=action_noise,
)
runner.setup(algo, env)
last_avg_ret = runner.train(n_epochs=10,
n_epoch_cycles=20,
batch_size=100)
assert last_avg_ret > 60
env.close()
def test_batch_sampler(self):
max_cpus = 8
with LocalRunner(max_cpus=max_cpus) as runner:
env = TfEnv(env_name='CartPole-v1')
policy = CategoricalMLPPolicy(name="policy",
env_spec=env.spec,
hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = VPG(env=env,
policy=policy,
baseline=baseline,
max_path_length=1,
whole_paths=True,
discount=0.99)
runner.setup(algo,
env,
sampler_cls=BatchSampler,
sampler_args={'n_envs': max_cpus})
try:
runner.initialize_tf_vars()
except BaseException:
raise self.failureException(
"LocalRunner should be able to initialize tf variables.")
runner.start_worker()
paths = runner.sampler.obtain_samples(0, 8)
self.assertGreaterEqual(
len(paths), max_cpus, "BatchSampler should sample more than "
"max_cpus=%d trajectories" % max_cpus)
def run_task(*_):
with LocalRunner() as runner:
env = PointEnv(goal=(3, 3), random_start=True)
env = TfEnv(env)
policy = GaussianMLPPolicy(name="policy",
env_spec=env.spec,
hidden_sizes=(64, 64),
init_std=20,
std_share_network=False,
adaptive_std=True)
baseline = GaussianMLPBaseline(env_spec=env,
include_action_to_input=False)
algo = PPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=1024, # 4096
max_path_length=50,
n_itr=1500,
discount=0.99,
step_size=0.2,
policy_ent_coeff=1e-6,
use_mpc_es=True,
)
runner.setup(algo, env)
runner.train(n_epochs=1500, batch_size=1024, plot=True)
def test_ppo_pendulum_with_model(self):
"""Test PPO with model, with Pendulum environment."""
with LocalRunner(self.sess) as runner:
env = TfEnv(normalize(gym.make('InvertedDoublePendulum-v2')))
policy = GaussianMLPPolicyWithModel(
env_spec=env.spec,
hidden_sizes=(64, 64),
hidden_nonlinearity=tf.nn.tanh,
output_nonlinearity=None,
)
baseline = GaussianMLPBaselineWithModel(
env_spec=env.spec,
regressor_args=dict(hidden_sizes=(32, 32)),
)
algo = PPO(
env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
lr_clip_range=0.01,
optimizer_args=dict(batch_size=32, max_epochs=10),
)
runner.setup(algo, env)
last_avg_ret = runner.train(n_epochs=10, batch_size=2048)
assert last_avg_ret > 30
env.close()
def run_task(*_):
with LocalRunner() as runner:
env = TfEnv(env_name='CartPole-v1')
policy = CategoricalLSTMPolicy(
name='policy',
env_spec=env.spec,
lstm_layer_cls=L.TfBasicLSTMLayer,
# gru_layer_cls=L.GRULayer,
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
max_kl_step=0.01,
optimizer=ConjugateGradientOptimizer,
optimizer_args=dict(
hvp_approach=FiniteDifferenceHvp(base_eps=1e-5)))
runner.setup(algo, env)
runner.train(n_epochs=100, batch_size=4000)
def test_cma_es_cartpole(self):
"""Test CMAES with Cartpole-v1 environment."""
with LocalRunner() as runner:
env = TfEnv(env_name="CartPole-v1")
policy = CategoricalMLPPolicy(
name="policy", env_spec=env.spec, hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
runner.initialize_tf_vars()
n_samples = 20
algo = CMAES(
env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
n_samples=n_samples)
runner.setup(algo, env, sampler_cls=OnPolicyVectorizedSampler)
runner.train(n_epochs=1, batch_size=1000, n_epoch_cycles=n_samples)
# No assertion on return because CMAES is not stable.
env.close()
def setup_method(self):
"""Setup method which is called before every test."""
self._env = GarageEnv(gym.make('InvertedDoublePendulum-v2'))
self._runner = LocalRunner(snapshot_config)
self._policy = GaussianMLPPolicy(env_spec=self._env.spec,
hidden_sizes=[64, 64],
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
self._params = {
'env_spec': self._env.spec,
'policy': self._policy,
'baseline': LinearFeatureBaseline(env_spec=self._env.spec),
'max_path_length': 100,
'discount': 0.99,
}
def run_task(*_):
"""Train CEM with Cartpole-v1 environment."""
with LocalRunner() as runner:
env = TfEnv(env_name='CartPole-v1')
policy = CategoricalMLPPolicy(
name='policy', env_spec=env.spec, hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
runner.initialize_tf_vars()
n_samples = 20
algo = CEM(
env_spec=env.spec,
policy=policy,
baseline=baseline,
best_frac=0.05,
max_path_length=100,
n_samples=n_samples)
runner.setup(algo, env, sampler_cls=OnPolicyVectorizedSampler)
# NOTE: make sure that n_epoch_cycles == n_samples !
runner.train(n_epochs=100, batch_size=1000, n_epoch_cycles=n_samples)
def test_reps_cartpole(self):
"""Test REPS with gym Cartpole environment."""
with LocalRunner(self.sess) as runner:
env = TfEnv(gym.make('CartPole-v0'))
policy = CategoricalMLPPolicy(env_spec=env.spec,
hidden_sizes=[32, 32])
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = REPS(env_spec=env.spec,
policy=policy,
baseline=baseline,
batch_size=4000,
max_path_length=100,
n_itr=10,
discount=0.99,
max_kl_step=1e6)
runner.setup(algo, env)
last_avg_ret = runner.train(n_epochs=10, batch_size=4000)
assert last_avg_ret > 5
env.close()
def test_npo_pendulum(self):
"""Test NPO with Pendulum environment."""
with LocalRunner(self.sess) as runner:
env = TfEnv(normalize(gym.make('InvertedDoublePendulum-v2')))
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(64, 64),
hidden_nonlinearity=tf.nn.tanh,
output_nonlinearity=None,
)
baseline = GaussianMLPBaseline(
env_spec=env.spec,
regressor_args=dict(hidden_sizes=(32, 32)),
)
algo = NPO(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
gae_lambda=0.98,
policy_ent_coeff=0.0)
runner.setup(algo, env)
last_avg_ret = runner.train(n_epochs=10, batch_size=2048)
assert last_avg_ret > 20
env.close()
def test_categorical_policies(self, policy_cls):
with LocalRunner(self.sess) as runner:
env = TfEnv(normalize(gym.make("CartPole-v0")))
policy = policy_cls(name="policy", env_spec=env.spec)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
step_size=0.01,
plot=True,
optimizer=ConjugateGradientOptimizer,
optimizer_args=dict(hvp_approach=FiniteDifferenceHvp(
base_eps=1e-5)),
)
runner.setup(algo, env)
runner.train(n_epochs=1, batch_size=4000)
env.close()
def run_task(snapshot_config, v):
"""
We wrap the main training loop in the run_task function so that
run_experiment can easily execute variants of the experiment on different
machines
"""
with LocalRunner(snapshot_config=snapshot_config) as runner:
env = TfEnv(env_name='CartPole-v1')
policy = CategoricalMLPPolicy(
env_spec=env.spec,
# The neural network policy should have two hidden layers,
# each with 32 hidden units.
hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
max_kl_step=v['step_size'],
)
runner.setup(algo=algo, env=env)
runner.train(
n_epochs=40,
batch_size=4000,
# Uncomment to enable plotting
# plot=True
)
def test_vpg_cartpole(self):
"""Test VPG with CartPole-v1 environment."""
with LocalRunner(sess=self.sess) as runner:
env = TfEnv(env_name='CartPole-v1')
policy = CategoricalMLPPolicy(name='policy',
env_spec=env.spec,
hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = VPG(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
optimizer_args=dict(
tf_optimizer_args=dict(learning_rate=0.01, )))
runner.setup(algo, env)
last_avg_ret = runner.train(n_epochs=10, batch_size=10000)
assert last_avg_ret > 90
env.close()
def test_ppo_pendulum(self):
"""Test PPO with Pendulum environment."""
deterministic.set_seed(0)
runner = LocalRunner(snapshot_config)
algo = PPO(env_spec=self.env.spec,
policy=self.policy,
value_function=self.value_function,
max_episode_length=100,
discount=0.99,
gae_lambda=0.97,
lr_clip_range=2e-1)
runner.setup(algo, self.env, sampler_cls=LocalSampler)
last_avg_ret = runner.train(n_epochs=10, batch_size=100)
assert last_avg_ret > 0
def test_ppo_pendulum_gru_with_model(self):
"""Test PPO with Pendulum environment and GRU policy."""
with LocalRunner(sess=self.sess) as runner:
env = TfEnv(normalize(gym.make('InvertedDoublePendulum-v2')))
policy = GaussianGRUPolicyWithModel(env_spec=env.spec, )
baseline = GaussianMLPBaseline(
env_spec=env.spec,
regressor_args=dict(hidden_sizes=(32, 32)),
)
algo = PPO(
env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
gae_lambda=0.95,
lr_clip_range=0.2,
optimizer_args=dict(
batch_size=32,
max_epochs=10,
),
stop_entropy_gradient=True,
entropy_method='max',
policy_ent_coeff=0.02,
center_adv=False,
)
runner.setup(algo, env)
last_avg_ret = runner.train(n_epochs=10, batch_size=2048)
assert last_avg_ret > 80
env.close()
def test_dm_control_tf_policy(self):
task = ALL_TASKS[0]
with LocalRunner(self.sess) as runner:
env = TfEnv(DmControlEnv.from_suite(*task))
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(32, 32),
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=5,
discount=0.99,
max_kl_step=0.01,
)
runner.setup(algo, env)
runner.train(n_epochs=1, batch_size=10)
env.close()
def run_task(vv):
with LocalRunner() as runner:
env = TfEnv(normalize(gym.make('HalfCheetah-v1')))
policy = GaussianMLPPolicy(env_spec=env.spec,
hidden_sizes=(32, 32),
name="policy")
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
step_size=vv["step_size"],
)
runner.setup(algo=algo, env=env)
runner.train(
n_epochs=40,
batch_size=4000,
# Uncomment to enable plotting
# plot=True
)
def test_sac_inverted_pendulum():
"""Test Sac performance on inverted pendulum."""
# pylint: disable=unexpected-keyword-arg
env = GarageEnv(normalize(gym.make('InvertedDoublePendulum-v2')))
deterministic.set_seed(0)
policy = TanhGaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=[32, 32],
hidden_nonlinearity=torch.nn.ReLU,
output_nonlinearity=None,
min_std=np.exp(-20.),
max_std=np.exp(2.),
)
qf1 = ContinuousMLPQFunction(env_spec=env.spec,
hidden_sizes=[32, 32],
hidden_nonlinearity=F.relu)
qf2 = ContinuousMLPQFunction(env_spec=env.spec,
hidden_sizes=[32, 32],
hidden_nonlinearity=F.relu)
replay_buffer = SimpleReplayBuffer(env_spec=env.spec,
size_in_transitions=int(1e6),
time_horizon=1)
runner = LocalRunner(snapshot_config=snapshot_config)
sac = SAC(env_spec=env.spec,
policy=policy,
qf1=qf1,
qf2=qf2,
gradient_steps_per_itr=100,
max_path_length=100,
use_automatic_entropy_tuning=True,
replay_buffer=replay_buffer,
min_buffer_size=1e3,
target_update_tau=5e-3,
discount=0.99,
buffer_batch_size=64,
reward_scale=1.,
steps_per_epoch=2)
runner.setup(sac, env, sampler_cls=LocalSampler)
if torch.cuda.is_available():
tu.set_gpu_mode(True)
else:
tu.set_gpu_mode(False)
sac.to()
ret = runner.train(n_epochs=12, batch_size=200, plot=False)
assert ret > 85
def run_task(snapshot_config, *_):
"""Run task."""
with LocalRunner(snapshot_config=snapshot_config) as runner:
n_epochs = 100
n_epoch_cycles = 20
sampler_batch_size = 500
num_timesteps = n_epochs * n_epoch_cycles * sampler_batch_size
env = gym.make('PongNoFrameskip-v4')
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)
env = TfEnv(env)
replay_buffer = SimpleReplayBuffer(
env_spec=env.spec, size_in_transitions=int(5e4), time_horizon=1)
qf = DiscreteCNNQFunction(
env_spec=env.spec,
filter_dims=(8, 4, 3),
num_filters=(32, 64, 64),
strides=(4, 2, 1),
dueling=False)
policy = DiscreteQfDerivedPolicy(env_spec=env.spec, qf=qf)
epilson_greedy_strategy = EpsilonGreedyStrategy(
env_spec=env.spec,
total_timesteps=num_timesteps,
max_epsilon=1.0,
min_epsilon=0.02,
decay_ratio=0.1)
algo = DQN(
env_spec=env.spec,
policy=policy,
qf=qf,
exploration_strategy=epilson_greedy_strategy,
replay_buffer=replay_buffer,
qf_lr=1e-4,
discount=0.99,
min_buffer_size=int(1e4),
double_q=False,
n_train_steps=500,
n_epoch_cycles=n_epoch_cycles,
target_network_update_freq=2,
buffer_batch_size=32)
runner.setup(algo, env)
runner.train(
n_epochs=n_epochs,
n_epoch_cycles=n_epoch_cycles,
batch_size=sampler_batch_size)
