wehub-resource-sync
93 lines
3.5 KiB
Python
93 lines
3.5 KiB
Python
from collections import Counter
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from typing import Any, List, Optional
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import gymnasium as gym
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from ray.rllib.connectors.connector_v2 import ConnectorV2
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from ray.rllib.core.rl_module.rl_module import RLModule
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from ray.rllib.utils.typing import EpisodeType
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class CountBasedCuriosity(ConnectorV2):
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"""Learner ConnectorV2 piece to compute intrinsic rewards based on obs counts.
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Add this connector piece to your Learner pipeline, through your algo config:
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```
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config.training(
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learner_connector=lambda obs_sp, act_sp: CountBasedCuriosity()
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)
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```
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Intrinsic rewards are computed on the Learner side based on naive observation
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counts, which is why this connector should only be used for simple environments
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with a reasonable number of possible observations. The intrinsic reward for a given
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timestep is:
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r(i) = intrinsic_reward_coeff * (1 / C(obs(i)))
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where C is the total (lifetime) count of the obs at timestep i.
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The intrinsic reward is added to the extrinsic reward and saved back into the
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episode (under the main "rewards" key).
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Note that the computation and saving back to the episode all happens before the
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actual train batch is generated from the episode data. Thus, the Learner and the
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RLModule used do not take notice of the extra reward added.
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If you would like to use a more sophisticated mechanism for intrinsic reward
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computations, take a look at the `EuclidianDistanceBasedCuriosity` connector piece
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at `ray.rllib.examples.connectors.classes.euclidian_distance_based_curiosity`
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"""
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def __init__(
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self,
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input_observation_space: Optional[gym.Space] = None,
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input_action_space: Optional[gym.Space] = None,
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*,
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intrinsic_reward_coeff: float = 1.0,
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**kwargs,
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):
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"""Initializes a CountBasedCuriosity instance.
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Args:
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intrinsic_reward_coeff: The weight with which to multiply the intrinsic
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reward before adding (and saving) it back to the main (extrinsic)
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reward of the episode at each timestep.
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"""
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super().__init__(input_observation_space, input_action_space)
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# Naive observation counter.
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self._counts = Counter()
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self.intrinsic_reward_coeff = intrinsic_reward_coeff
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def __call__(
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self,
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*,
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rl_module: RLModule,
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batch: Any,
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episodes: List[EpisodeType],
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explore: Optional[bool] = None,
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shared_data: Optional[dict] = None,
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**kwargs,
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) -> Any:
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# Loop through all episodes and change the reward to
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# [reward + intrinsic reward]
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for sa_episode in self.single_agent_episode_iterator(
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episodes=episodes, agents_that_stepped_only=False
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):
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# Loop through all observations, except the last one.
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observations = sa_episode.get_observations(slice(None, -1))
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# Get all respective extrinsic rewards.
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rewards = sa_episode.get_rewards()
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for i, (obs, rew) in enumerate(zip(observations, rewards)):
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# Add 1 to obs counter.
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obs = tuple(obs)
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self._counts[obs] += 1
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# Compute the count-based intrinsic reward and add it to the extrinsic
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# reward.
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rew += self.intrinsic_reward_coeff * (1 / self._counts[obs])
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# Store the new reward back to the episode (under the correct
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# timestep/index).
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sa_episode.set_rewards(new_data=rew, at_indices=i)
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return batch
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