Reinforcement Learning With Temporal Logic Rewards
This addresses the challenge of designing reward functions for complex robotic tasks, offering a more automated and expressive method, though it is incremental as it builds on existing temporal logic and RL techniques.
The paper tackles the problem of specifying complex tasks for robots in reinforcement learning by using temporal logic to automatically generate reward functions, showing in simulations that this approach leads to faster learning and policies that better satisfy tasks, with a Baxter robot successfully learning a toast-placing task.
Reinforcement learning (RL) depends critically on the choice of reward functions used to capture the de- sired behavior and constraints of a robot. Usually, these are handcrafted by a expert designer and represent heuristics for relatively simple tasks. Real world applications typically involve more complex tasks with rich temporal and logical structure. In this paper we take advantage of the expressive power of temporal logic (TL) to specify complex rules the robot should follow, and incorporate domain knowledge into learning. We propose Truncated Linear Temporal Logic (TLTL) as specifications language, that is arguably well suited for the robotics applications, together with quantitative semantics, i.e., robustness degree. We propose a RL approach to learn tasks expressed as TLTL formulae that uses their associated robustness degree as reward functions, instead of the manually crafted heuristics trying to capture the same specifications. We show in simulated trials that learning is faster and policies obtained using the proposed approach outperform the ones learned using heuristic rewards in terms of the robustness degree, i.e., how well the tasks are satisfied. Furthermore, we demonstrate the proposed RL approach in a toast-placing task learned by a Baxter robot.