Reinforcement Learning With Temporal Logic Rewards. (arXiv:1612.03471v1 [cs.AI])

The reward function plays a critical role in rein- forcement learning (RL). It is a place where designers specify the desired behavior and impose important constraints for the system. While most reward functions used in the current RL literature have been quite simple for relatively simple tasks, real world applications typically involve tasks that are logically more complex. In this paper we take advantage of the expressive power of temporal logic (TL) to express complex rules the system should follow, or incorporate domain knowledge into learning. We propose a TL that we argue is suitable for robotic task specification in RL. We also present the concept of one-step robustness, and show that the problem of sparse reward that occurs when using TL specification as the reward function can be alleviated while leaving the resultant policy invariant. Simulated manipulation tasks are used to illustrate RL with the proposed TL and the one-step robustness.

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