Learning to Switch Among Agents in a Team via 2-Layer Markov Decision Processes
This addresses the challenge of flexible automation in multi-agent systems, though it is incremental as it builds on existing reinforcement learning methods.
The paper tackles the problem of enabling reinforcement learning agents to operate under varying automation levels by learning to switch control among agents, achieving sublinear regret with respect to the optimal switching policy and demonstrating superiority over problem-agnostic algorithms in simulations.
Reinforcement learning agents have been mostly developed and evaluated under the assumption that they will operate in a fully autonomous manner -- they will take all actions. In this work, our goal is to develop algorithms that, by learning to switch control between agents, allow existing reinforcement learning agents to operate under different automation levels. To this end, we first formally define the problem of learning to switch control among agents in a team via a 2-layer Markov decision process. Then, we develop an online learning algorithm that uses upper confidence bounds on the agents' policies and the environment's transition probabilities to find a sequence of switching policies. The total regret of our algorithm with respect to the optimal switching policy is sublinear in the number of learning steps and, whenever multiple teams of agents operate in a similar environment, our algorithm greatly benefits from maintaining shared confidence bounds for the environments' transition probabilities and it enjoys a better regret bound than problem-agnostic algorithms. Simulation experiments in an obstacle avoidance task illustrate our theoretical findings and demonstrate that, by exploiting the specific structure of the problem, our proposed algorithm is superior to problem-agnostic algorithms.