def test_replay_buffer(n_episodes, batch_size, maxlen, create_mask, mask_size):
env = gym.make("CartPole-v0")
buffer = ReplayBuffer(maxlen,
env,
create_mask=create_mask,
mask_size=mask_size)
total_step = 0
for episode in range(n_episodes):
observation, reward, terminal = env.reset(), 0.0, False
while not terminal:
action = env.action_space.sample()
buffer.append(observation.astype("f4"), action, reward, terminal)
observation, reward, terminal, _ = env.step(action)
total_step += 1
buffer.append(observation.astype("f4"), action, reward, terminal)
total_step += 1
assert len(buffer) == maxlen
# check static dataset conversion
dataset = buffer.to_mdp_dataset()
transitions = []
for episode in dataset:
transitions += episode.transitions
assert len(transitions) >= len(buffer)
observation_shape = env.observation_space.shape
batch = buffer.sample(batch_size)
assert len(batch) == batch_size
assert batch.observations.shape == (batch_size, ) + observation_shape
assert batch.actions.shape == (batch_size, )
assert batch.rewards.shape == (batch_size, 1)
assert batch.next_observations.shape == (batch_size, ) + observation_shape
assert batch.next_actions.shape == (batch_size, )
assert batch.next_rewards.shape == (batch_size, 1)
assert batch.terminals.shape == (batch_size, 1)
assert isinstance(batch.observations, np.ndarray)
assert isinstance(batch.next_observations, np.ndarray)
if create_mask:
assert batch.masks.shape == (mask_size, batch_size, 1)
else:
assert batch.masks is None
def train(params):
# setup algorithm
if pretrain:
dqn = DQN(batch_size=params.get("batch_size"),
learning_rate=params.get("learning_rate"),
target_update_interval=params.get("target_update_interval"),
q_func_factory=QRQFunctionFactory(
n_quantiles=params.get("n_quantiles")),
n_steps=params.get("train_freq"),
gamma=params.get("gamma"),
n_critics=1,
target_reduction_type="min",
use_gpu=True)
# setup replay buffer
buffer = ReplayBuffer(maxlen=params.get("buffer_size"), env=env)
# setup explorers
explorer = LinearDecayEpsilonGreedy(
start_epsilon=1.0,
end_epsilon=params.get("exploration_final_eps"),
duration=100000)
# start training
dqn.fit_online(
env,
buffer,
n_steps=params.get("train_steps"),
explorer=
explorer, # you don't need this with probablistic policy algorithms
tensorboard_dir=log_dir,
eval_env=eval_env)
print("Saving Model")
dqn.save_model(exp_name)
print("convert buffer to dataset")
dataset = buffer.to_mdp_dataset()
# save MDPDataset
dataset.dump('{0}.h5'.format(exp_name))
print("Loading Dataset for Offline Training")
dataset = d3rlpy.dataset.MDPDataset.load('{0}.h5'.format(exp_name))
train_episodes, test_episodes = train_test_split(dataset, test_size=0.2)
# The dataset can then be used to train a d3rlpy model
cql = DiscreteCQL(learning_rate=6.25e-05,
encoder_factory='default',
q_func_factory='mean',
batch_size=32,
n_frames=1,
n_steps=1,
gamma=0.99,
n_critics=1,
bootstrap=False,
share_encoder=False,
target_reduction_type='min',
target_update_interval=8000,
use_gpu=True,
scaler=None,
augmentation=None,
generator=None,
impl=None)
cql_exp = params.get("model_name") + "_offline_" + params.get(
"environment")
cql_log = '../../../logs/' + cql_exp
cql.fit(dataset.episodes,
eval_episodes=test_episodes,
n_epochs=1000,
scorers={
'environment': evaluate_on_environment(env, epsilon=0.05),
'td_error': td_error_scorer,
'discounted_advantage': discounted_sum_of_advantage_scorer,
'value_scale': average_value_estimation_scorer,
},
tensorboard_dir=cql_log)
cql.save_model(cql_exp)
