def test_ddpg_trainer(self):
environment = GridworldContinuous()
samples = environment.generate_samples(200000, 1.0)
epochs = 3
trainer = DDPGTrainer(
self.get_ddpg_parameters(),
environment.normalization,
environment.normalization_action,
)
evaluator = GridworldDDPGEvaluator(environment, True)
tdps = environment.preprocess_samples(samples, self.minibatch_size)
for epoch in range(epochs):
print("On epoch {} of {}".format(epoch + 1, epochs))
critic_predictor = trainer.predictor()
evaluator.evaluate_critic(critic_predictor)
for tdp in tdps:
training_samples = [
tdp.states,
tdp.actions,
tdp.rewards.flatten(),
tdp.next_states,
None,
1 - tdp.not_terminals.flatten(), # done
None,
None,
[1 for i in range(len(tdp.states))], # time diff
]
trainer.train(training_samples)
critic_predictor = trainer.predictor()
error = evaluator.evaluate_critic(critic_predictor)
print("gridworld MAE: {0:.3f}".format(error))
def test_ddpg_trainer(self):
environment = GridworldContinuous()
samples = environment.generate_samples(500000, 0.25)
trainer = DDPGTrainer(
self.get_ddpg_parameters(),
environment.normalization,
environment.normalization_action,
environment.min_action_range,
environment.max_action_range,
)
evaluator = GridworldDDPGEvaluator(environment, True, DISCOUNT, False,
samples)
tdps = environment.preprocess_samples(samples, self.minibatch_size)
critic_predictor = trainer.predictor(actor=False)
evaluator.evaluate_critic(critic_predictor)
for tdp in tdps:
tdp.rewards = tdp.rewards.flatten()
tdp.not_terminals = tdp.not_terminals.flatten()
trainer.train(tdp)
# Make sure actor predictor works
actor = trainer.predictor(actor=True)
evaluator.evaluate_actor(actor)
# Evaluate critic predicor for correctness
critic_predictor = trainer.predictor(actor=False)
error = evaluator.evaluate_critic(critic_predictor)
print("gridworld MAE: {0:.3f}".format(error))