I have this custom callback to log the reward in my custom vectorized environment, but the reward appears in console as always [0] and is not logged in tensorboard at all
class TensorboardCallback(BaseCallback):
"""
Custom callback for plotting additional values in tensorboard.
"""
def __init__(self, verbose=0):
super(TensorboardCallback, self).__init__(verbose)
def _on_step(self) -> bool:
self.logger.record('reward', self.training_env.get_attr('total_reward'))
return True
And this is part of the main function
model = PPO(
"MlpPolicy", env,
learning_rate=3e-4,
policy_kwargs=policy_kwargs,
verbose=1,
# as the environment is not serializable, we need to set a new instance of the environment
loaded_model = model = PPO.load("model", env=env)
loaded_model.set_env(env)
# and continue training
loaded_model.learn(1e+6, callback=TensorboardCallback())
tensorboard_log="./tensorboard/")
You need to add [0]
as indexing,
so where you wrote self.logger.record('reward', self.training_env.get_attr('total_reward'))
you just need to index with self.logger.record('reward', self.training_env.get_attr ('total_reward')[0]
)
class TensorboardCallback(BaseCallback):
"""
Custom callback for plotting additional values in tensorboard.
"""
def __init__(self, verbose=0):
super(TensorboardCallback, self).__init__(verbose)
def _on_step(self) -> bool:
self.logger.record('reward', self.training_env.get_attr('total_reward')[0])
return True