Papers › Reinforcement Learning Generalization with Surprise Minimization

Reinforcement Learning Generalization with Surprise Minimization

26 Apr 2020arXiv:2004.12399archive 2025-07-28

Jerry Zikun Chen

Generalization remains a challenging problem for deep reinforcement learning algorithms, which are often trained and tested on the same set of deterministic game environments. When test environments are unseen and perturbed but the nature of the task remains the same, generalization gaps can arise. In this work, we propose and evaluate a surprise minimizing agent on a generalization benchmark to show an additional reward learned from a simple density model can show robustness in procedurally generated game environments that provide constant source of entropy and stochasticity.

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Deep Reinforcement Learning Reinforcement Learning Reinforcement Learning (RL)reinforcement-learning

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