def test_compare():
dataset, _ = get_cartpole()
train_episodes = dataset.episodes[:10]
test_episodes = dataset.episodes[-10:]
algo = DQN(n_epochs=1)
algo.fit(train_episodes, logdir='test_data')
base_algo = DQN(n_epochs=1)
base_algo.fit(train_episodes, logdir='test_data')
score = _compare(algo, base_algo, test_episodes, True)
def test_evaluate():
dataset, _ = get_cartpole()
train_episodes = dataset.episodes[:10]
test_episodes = dataset.episodes[-10:]
algo = DQN(n_epochs=1)
algo.fit(train_episodes, logdir='test_data')
scores = _evaluate(algo, test_episodes, True)
eval_keys = [
'td_error', 'advantage', 'average_value', 'value_std', 'action_match'
]
for key in eval_keys:
assert key in scores
def main(args):
dataset, env = get_atari(args.dataset)
d3rlpy.seed(args.seed)
train_episodes, test_episodes = train_test_split(dataset, test_size=0.2)
dqn = DQN(
n_frames=4, # frame stacking
q_func_type=args.q_func_type,
scaler='pixel',
use_gpu=args.gpu)
dqn.fit(train_episodes,
eval_episodes=test_episodes,
n_epochs=100,
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
})
def main(args):
dataset, env = get_atari(args.dataset)
d3rlpy.seed(args.seed)
train_episodes, test_episodes = train_test_split(dataset, test_size=0.2)
device = None if args.gpu is None else Device(args.gpu)
dqn = DQN(n_epochs=100,
q_func_type=args.q_func_type,
scaler='pixel',
use_batch_norm=False,
use_gpu=device)
dqn.fit(train_episodes,
eval_episodes=test_episodes,
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
})
from d3rlpy.algos import DQN from d3rlpy.datasets import get_cartpole from d3rlpy.metrics.scorer import evaluate_on_environment # obtain dataset dataset, env = get_cartpole() # setup algorithm dqn = DQN(n_epochs=1) # train dqn.fit(dataset.episodes) # evaluate trained algorithm evaluate_on_environment(env, render=True)(dqn)
from d3rlpy.algos import DQN from d3rlpy.datasets import get_cartpole from d3rlpy.metrics.scorer import evaluate_on_environment # obtain dataset dataset, env = get_cartpole() # setup algorithm dqn = DQN() # train dqn.fit(dataset.episodes, n_epochs=1) # evaluate trained algorithm evaluate_on_environment(env, render=True)(dqn)