def test_cartpole_reinforce(self):
# TODO(@badri) Parameterize this test
env = Gym("CartPole-v0")
norm = build_normalizer(env)
from reagent.net_builder.discrete_dqn.fully_connected import FullyConnected
net_builder = FullyConnected(sizes=[8], activations=["linear"])
cartpole_scorer = net_builder.build_q_network(
state_feature_config=None,
state_normalization_data=norm["state"],
output_dim=len(norm["action"].dense_normalization_parameters),
)
from reagent.gym.policies.samplers.discrete_sampler import SoftmaxActionSampler
policy = Policy(scorer=cartpole_scorer, sampler=SoftmaxActionSampler())
from reagent.training.reinforce import Reinforce, ReinforceParams
from reagent.optimizer.union import classes
trainer = Reinforce(
policy,
ReinforceParams(gamma=0.995,
optimizer=classes["Adam"](lr=5e-3,
weight_decay=1e-3)),
)
run_test_episode_buffer(
env,
policy,
trainer,
num_train_episodes=500,
passing_score_bar=180,
num_eval_episodes=100,
)
def test_toyvm(self):
pl.seed_everything(SEED)
env = ToyVM(slate_size=5, initial_seed=SEED)
from reagent.models import MLPScorer
slate_scorer = MLPScorer(input_dim=3,
log_transform=True,
layer_sizes=[64],
concat=False)
from reagent.samplers import FrechetSort
policy = Policy(slate_scorer,
FrechetSort(log_scores=True, topk=5, equiv_len=5))
from reagent.optimizer.union import classes
from reagent.training.reinforce import Reinforce, ReinforceParams
trainer = Reinforce(
policy,
ReinforceParams(gamma=0,
optimizer=classes["Adam"](lr=1e-1,
weight_decay=1e-3)),
)
run_test_episode_buffer(
env,
policy,
trainer,
num_train_episodes=500,
passing_score_bar=120,
num_eval_episodes=100,
)