Reinforcement Learning (RL) agents require the specification of a reward
signal for learning behaviours. However, introduction of corrupt or stochastic
rewards can yield high variance in learning. Such corruption may be a direct
result of goal misspecification, randomness in the reward signal, or
correlation of the reward with external factors that are not known to the
agent. Corruption or stochasticity of the reward signal can be especially
problematic in robotics, where goal specification can be particularly difficult
for complex tasks. While many variance reduction techniques have been studied
to improve the robustness of the RL process, handling such stochastic or
corrupted reward structures remains difficult. As an alternative for handling
this scenario in model-free RL methods, we suggest using an estimator for both
rewards and value functions. We demonstrate that this improves performance
under corrupted stochastic rewards in both the tabular and non-linear function
approximation settings for a variety of noise types and environments. The use
of reward estimation is a robust and easy-to-implement improvement for handling
corrupted reward signals in model-free RL.

该研究提出使用奖励估算方法来应对机器人学习中复杂任务中可能出现的损坏或随机奖励信号，这种方法能够提高算法的稳定性和性能表现，并且应用范围广泛。