class PPOrl(object):
def __init__(self, env, env_config, config):
self.config = config
self.config['env_config'] = env_config
self.env = env(env_config)
self.agent = PPOTrainer(config=self.config, env=env)
def fit(self, checkpoint=None, n_iter=2000, save_checkpoint=10):
if checkpoint is None:
checkpoint = os.path.join(os.getcwd(), 'data/checkpoint_rl.pkl')
for idx in trange(n_iter):
result = self.agent.train()
LOGGER.warning('result: ', result)
if (idx + 1) % save_checkpoint == 0:
LOGGER.warning('Save checkpoint at: {}'.format(idx + 1))
state = self.agent.save_to_object()
with open(checkpoint, 'wb') as fp:
pickle.dump(state, fp, protocol=pickle.HIGHEST_PROTOCOL)
return result
def predict(self, checkpoint=None):
if checkpoint is not None:
with open(checkpoint, 'rb') as fp:
state = pickle.load(fp)
self.agent.restore_from_object(state)
done = False
episode_reward = 0
obs = self.env.reset()
actions = []
while not done:
action = self.agent.compute_action(obs)
actions.append(action)
obs, reward, done, info = self.env.step(action)
episode_reward += reward
results = {'action': actions, 'reward': episode_reward}
return results
"vf_clip_param": 10.0
}
last_improve = 150
iteration = 22
improved = 0
while True:
trainer = PPOTrainer(env="fire_mage", config=rnn_config)
print(dir(trainer))
#trainer.restore('./checkpoints_flush/checkpoint_379/checkpoint-379')
step = 0
best_val = 0.0
if False:
save_0 = trainer.save_to_object()
while True:
if False:
save_0 = trainer.save_to_object()
result = trainer.train()
while result['episode_reward_mean'] > best_val:
print('UPENING')
best_save = deepcopy(save_0)
best_val = result['episode_reward_mean']
save_0 = trainer.save_to_object()
trainer.save('./checkpoints_flush')
result = trainer.train()
print('REVERTING')
trainer.restore_from_object(best_save)
else:
result = trainer.train()
