def test_trainer_maxq(self):
environment = Gridworld()
maxq_sarsa_parameters = DiscreteActionModelParameters(
actions=environment.ACTIONS,
rl=RLParameters(gamma=DISCOUNT,
target_update_rate=0.5,
reward_burnin=10,
maxq_learning=True),
training=TrainingParameters(
layers=[-1, 1],
activations=['linear'],
minibatch_size=self.minibatch_size,
learning_rate=0.01,
optimizer='ADAM',
))
# construct the new trainer that using maxq
maxq_trainer = DiscreteActionTrainer(
maxq_sarsa_parameters,
environment.normalization,
)
states, actions, rewards, next_states, next_actions, is_terminal,\
possible_next_actions, reward_timelines = \
environment.generate_samples(100000, 1.0)
predictor = maxq_trainer.predictor()
tdps = environment.preprocess_samples(
states,
actions,
rewards,
next_states,
next_actions,
is_terminal,
possible_next_actions,
reward_timelines,
self.minibatch_size,
)
evaluator = GridworldEvaluator(environment, True)
print("Pre-Training eval", evaluator.evaluate(predictor))
self.assertGreater(evaluator.evaluate(predictor), 0.3)
for _ in range(2):
for tdp in tdps:
maxq_trainer.stream_tdp(tdp, None)
evaluator.evaluate(predictor)
print("Post-Training eval", evaluator.evaluate(predictor))
self.assertLess(evaluator.evaluate(predictor), 0.1)
def test_pure_q_learning_all_cheat(self):
q_learning_parameters = DiscreteActionModelParameters(
actions=self._env.ACTIONS,
rl=self._rl_parameters_all_cheat_maxq,
training=TrainingParameters(
layers=[self._env.width * self._env.height, 1],
activations=['linear'],
minibatch_size=self.minibatch_size,
learning_rate=0.05,
optimizer='SGD',
lr_policy='fixed'))
trainer = DiscreteActionTrainer(
q_learning_parameters,
self._env.normalization,
)
predictor = trainer.predictor()
policy = _build_policy(self._env, predictor, 1)
initial_state = self._env.reset()
iteration_result = _collect_samples(self._env, policy, 10000,
initial_state)
num_iterations = 50
for _ in range(num_iterations):
policy = _build_policy(self._env, predictor, 0)
tdps = self._env.preprocess_samples(
iteration_result.states,
iteration_result.actions,
iteration_result.rewards,
iteration_result.next_states,
iteration_result.next_actions,
iteration_result.is_terminals,
iteration_result.possible_next_actions,
None,
self.minibatch_size,
)
for tdp in tdps:
trainer.stream_tdp(tdp, None)
initial_state = iteration_result.current_state
initial_state = self._env.reset()
iteration_result = _collect_samples(self._env, policy, 10000,
initial_state)
self.assertTrue(np.all(np.array(iteration_result.actions) == 'C'))