def test_trainer_sarsa_enum(self):
environment = GridworldContinuousEnum()
states, actions, rewards, next_states, next_actions, is_terminal,\
possible_next_actions, reward_timelines = \
environment.generate_samples(100000, 1.0)
trainer = self.get_sarsa_trainer(environment)
predictor = trainer.predictor()
evaluator = GridworldContinuousEvaluator(environment, False)
tdps = environment.preprocess_samples(
states,
actions,
rewards,
next_states,
next_actions,
is_terminal,
possible_next_actions,
reward_timelines,
self.minibatch_size,
)
self.assertGreater(evaluator.evaluate(predictor), 0.15)
for tdp in tdps:
trainer.train_numpy(tdp, None)
evaluator.evaluate(predictor)
self.assertLess(evaluator.evaluate(predictor), 0.05)
def test_trainer_sarsa_enum(self):
environment = GridworldContinuousEnum()
samples = environment.generate_samples(100000, 1.0)
trainer = self.get_sarsa_trainer(environment)
predictor = trainer.predictor()
evaluator = GridworldContinuousEvaluator(environment, False, DISCOUNT,
False, samples)
tdps = environment.preprocess_samples(samples, self.minibatch_size)
for tdp in tdps:
trainer.train_numpy(tdp, None)
evaluator.evaluate(predictor)
self.assertLess(evaluator.evaluate(predictor), 0.15)
def test_trainer_sarsa_enum_factorized(self):
environment = GridworldContinuousEnum()
samples = environment.generate_samples(500000, 1.0, DISCOUNT)
trainer = self.get_sarsa_trainer(
environment, self.get_sarsa_parameters_factorized())
predictor = trainer.predictor()
evaluator = GridworldContinuousEvaluator(environment, False, DISCOUNT,
False, samples)
tdps = environment.preprocess_samples(samples, self.minibatch_size)
for tdp in tdps:
trainer.train(tdp)
predictor = trainer.predictor()
evaluator.evaluate(predictor)
self.assertLess(evaluator.evaluate(predictor), 0.15)
def test_trainer_sarsa_enum(self):
environment = GridworldContinuousEnum()
samples = environment.generate_samples(150000, 1.0)
trainer = self.get_sarsa_trainer(environment)
predictor = trainer.predictor()
evaluator = GridworldContinuousEvaluator(environment, False, DISCOUNT,
False, samples)
tdps = environment.preprocess_samples(samples, self.minibatch_size)
for tdp in tdps:
tdp.rewards = tdp.rewards.flatten()
tdp.not_terminals = tdp.not_terminals.flatten()
trainer.train(tdp)
predictor = trainer.predictor()
evaluator.evaluate(predictor)
self.assertLess(evaluator.evaluate(predictor), 0.15)