def perform_dqn_logging(self):
episode_rewards = get_wrapper_by_name(self.env,
"Monitor").get_episode_rewards()
episode_rewards_len = len(episode_rewards)
if episode_rewards_len > 0:
self.mean_episode_reward = np.mean(episode_rewards[-100:])
if episode_rewards_len > 100:
self.best_mean_episode_reward = max(self.best_mean_episode_reward,
self.mean_episode_reward)
logs = OrderedDict()
logs["Train_EnvstepsSoFar"] = self.agent.t
print("Timestep %d" % (self.agent.t, ))
if self.mean_episode_reward > -5000:
logs["Train_AverageReturn"] = np.mean(self.mean_episode_reward)
print("mean reward (100 episodes) %f" % self.mean_episode_reward)
if self.best_mean_episode_reward > -5000:
logs["Train_BestReturn"] = np.mean(self.best_mean_episode_reward)
print("best mean reward %f" % self.best_mean_episode_reward)
print(f'episode len: {episode_rewards_len}')
if self.start_time is not None:
time_since_start = (time.time() - self.start_time)
print("running time %f" % time_since_start)
logs["TimeSinceStart"] = time_since_start
sys.stdout.flush()
for key, value in logs.items():
print('{} : {}'.format(key, value))
self.logger.log_scalar(value, key, self.agent.t)
print('Done logging...\n\n')
self.logger.flush()
def perform_dqn_logging(self):
episode_rewards = get_wrapper_by_name(self.env, "Monitor").get_episode_rewards()
if len(episode_rewards) > 0:
self.mean_episode_reward = np.mean(episode_rewards[-100:])
if len(episode_rewards) > 100:
self.best_mean_episode_reward = max(self.best_mean_episode_reward, self.mean_episode_reward)
logs = OrderedDict()
logs["Train_EnvstepsSoFar"] = self.agent.t
print("Timestep %d" % (self.agent.t,))
if self.mean_episode_reward > -5000:
logs["Train_AverageReturn"] = np.mean(self.mean_episode_reward)
print("mean reward (100 episodes) %f" % self.mean_episode_reward)
if self.best_mean_episode_reward > -5000:
logs["Train_BestReturn"] = np.mean(self.best_mean_episode_reward)
print("best mean reward %f" % self.best_mean_episode_reward)
if self.start_time is not None:
time_since_start = (time.time() - self.start_time)
print("running time %f" % time_since_start)
logs["TimeSinceStart"] = time_since_start
if self.params['use_wandb'] == 1:
wandb.log(logs)
def perform_dqn_logging(self, all_logs):
last_log = all_logs[-1]
episode_rewards = get_wrapper_by_name(self.env,
"Monitor").get_episode_rewards()
print("0: ", len(episode_rewards)) #added
if len(episode_rewards) > 0:
self.mean_episode_reward = np.mean(episode_rewards[-100:])
print("1: ", self.mean_episode_reward) # added
if len(episode_rewards) > 100:
self.best_mean_episode_reward = max(self.best_mean_episode_reward,
self.mean_episode_reward)
print("2: ", self.mean_episode_reward) # added
logs = OrderedDict()
logs["Train_EnvstepsSoFar"] = self.agent.t
print("Timestep %d" % (self.agent.t, ))
if self.mean_episode_reward > -5000:
logs["Train_AverageReturn"] = np.mean(self.mean_episode_reward)
print("mean reward (100 episodes) %f" % self.mean_episode_reward)
if self.best_mean_episode_reward > -5000:
logs["Train_BestReturn"] = np.mean(self.best_mean_episode_reward)
print("best mean reward %f" % self.best_mean_episode_reward)
if self.start_time is not None:
time_since_start = (time.time() - self.start_time)
print("running time %f" % time_since_start)
logs["TimeSinceStart"] = time_since_start
logs.update(last_log)
eval_paths, eval_envsteps_this_batch = utils.sample_trajectories(
self.eval_env, self.agent.eval_policy,
self.params['eval_batch_size'], self.params['ep_len'])
eval_returns = [eval_path["reward"].sum() for eval_path in eval_paths]
eval_ep_lens = [len(eval_path["reward"]) for eval_path in eval_paths]
logs["Eval_AverageReturn"] = np.mean(eval_returns)
logs["Eval_StdReturn"] = np.std(eval_returns)
logs["Eval_MaxReturn"] = np.max(eval_returns)
logs["Eval_MinReturn"] = np.min(eval_returns)
logs["Eval_AverageEpLen"] = np.mean(eval_ep_lens)
logs['Buffer size'] = self.agent.replay_buffer.num_in_buffer
sys.stdout.flush()
for key, value in logs.items():
print('{} : {}'.format(key, value))
self.logger.log_scalar(value, key, self.agent.t)
print('Done logging...\n\n')
self.logger.flush()
def perform_dqn_logging(self, all_logs):
last_log = all_logs[-1]
episode_rewards = get_wrapper_by_name(self.env,
"Monitor").get_episode_rewards()
if len(episode_rewards) > 0:
self.mean_episode_reward = np.mean(episode_rewards[-100:])
if len(episode_rewards) > 100:
self.best_mean_episode_reward = max(self.best_mean_episode_reward,
self.mean_episode_reward)
logs = OrderedDict()
logs["Train_EnvstepsSoFar"] = self.agent.t
print("Timestep %d" % (self.agent.t, ))
if self.mean_episode_reward > -5000:
logs["Train_AverageReturn"] = np.mean(self.mean_episode_reward)
print("mean reward (100 episodes) %f" % self.mean_episode_reward)
if self.best_mean_episode_reward > -5000:
logs["Train_BestReturn"] = np.mean(self.best_mean_episode_reward)
print("best mean reward %f" % self.best_mean_episode_reward)
if self.start_time is not None:
time_since_start = time.time() - self.start_time
print("running time %f" % time_since_start)
logs["TimeSinceStart"] = time_since_start
logs.update(last_log)
sys.stdout.flush()
for key, value in logs.items():
print("{} : {}".format(key, value))
self.logger.log_scalar(value, key, self.agent.t)
print("Done logging...\n\n")
self.logger.flush()
