Action Pick-up in Dynamic Action Space Reinforcement Learning

Understand

Most reinforcement learning algorithms are based on a key assumption that Markov decision processes (MDPs) are stationary.

• However, non-stationary MDPs with dynamic action space are omnipresent in real-world scenarios.
• Yet problems of dynamic action space reinforcement learning have been studied by many previous works, how to choose valuable actions from new and unseen actions to improve learning efficiency remains unaddressed.
• To tackle this problem, we propose an intelligent Action Pick-up (AP) algorithm to autonomously choose valuable actions that are most likely to boost performance from a set of new actions.