探究内在动机如何改变智能体行为，发现其会提升初始奖励但改变游戏策略。
Minding Motivation: The Effect of Intrinsic Motivation on Agent Behaviors
- 对比三种内在动机方法在迷你网格环境中的行为变化
- 内在动机使初始奖励上升，且改变智能体游戏方式
- 通用奖励匹配法可缓解部分奖励劫持问题
游戏对强化学习（RL）智能体构成挑战，因其奖励稀疏——奖励仅在长时间精心操作后获得。内在动机（IM）通过引入探索奖励来缓解这一问题。然而，IM也引发‘奖励劫持’现象，即智能体为最大化新奖励而偏离正常游戏目标。当前尚不清楚IM究竟在多大程度上改变了智能体行为。本研究首次通过实证评估三种IM技术在MiniGrid游戏环境中的影响，并与通用奖励匹配（GRM）方法进行比较。该方法可与任意内在奖励函数结合，保证最优性。结果表明，IM显著提升了初始奖励，同时改变了智能体的游戏策略；而GRM在部分场景中有效缓解了奖励劫持问题。
原文摘要 · Abstract (English)
Games are challenging for Reinforcement Learning~(RL) agents due to their reward-sparsity, as rewards are only obtainable after long sequences of deliberate actions. Intrinsic Motivation~(IM) methods -- which introduce exploration rewards -- are an effective solution to reward-sparsity. However, IM also causes an issue known as `reward hacking' where the agent optimizes for the new reward at the expense of properly playing the game. The larger problem is that reward hacking itself is largely unknown; there is no answer to whether, and to what extent, IM rewards change the behavior of RL agents. This study takes a first step by empirically evaluating the impact on behavior of three IM techniques on the MiniGrid game-like environment. We compare these IM models with Generalized Reward Matching~(GRM), a method that can be used with any intrinsic reward function to guarantee optimality. Our results suggest that IM causes noticeable change by increasing the initial rewards, but also altering the way the agent plays; and that GRM mitigated reward hacking in some scenarios.
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