Many practical reinforcement learning problems contain catastrophic states that the optimal policy visits infrequently or never. Even on toy problems, deep reinforcement learners periodically revisit these states, once they are forgotten under a new policy. In this paper, we introduce intrinsic fear, a learned reward shaping that accelerates deep reinforcement learning and guards oscillating policies against periodic catastrophes. Our approach incorporates a second model trained via supervised learning to predict the probability of imminent catastrophe. This score acts as a penalty on the Q-learning objective. Our theoretical analysis demonstrates that the perturbed objective yields the same average return under strong assumptions and an $\epsilon$-close average return under weaker assumptions. Our analysis also shows robustness to classification errors. Equipped with intrinsic fear, our DQNs solve the toy environments and improve on the Atari games Seaquest, Asteroids, and Freeway.
Shape reward with intrinsic motivation to avoid catastrophic states and mitigate catastrophic forgetting. An RL algorithm that combines the DQN algorithm with a fear model trained in parallel to predict catastropohic states. The paper studies catastrophic forgetting in RL, by emphasizing tasks where a DQN is able to learn to avoid catastrophic events as long as it avoids forgetting.