Model-Free Reinforcement Learning with Continuous Action in Practice

Thomas Degris (1) , Patrick M. Pilarski (2) , Richard S. Sutton (2)

1 Flowers - Flowing Epigenetic Robots and Systems
2 RLAI

Reinforcement learning methods are often con- sidered as a potential solution to enable a robot to adapt to changes in real time to an unpredictable environment. However, with continuous action, only a few existing algorithms are practical for real-time learning. In such a setting, most effective methods have used a parameterized policy structure, often with a separate parameterized value function. The goal of this paper is to assess such actor-critic methods to form a fully specified practical algorithm. Our specific contributions include 1) developing the extension of existing incremental policy-gradient algorithms to use eligibility traces, 2) an empir- ical comparison of the resulting algorithms using continuous actions, 3) the evaluation of a gradient-scaling technique that can significantly improve performance. Finally, we apply our actor-critic algorithm to learn on a robotic platform with a fast sensorimotor cycle (10ms). Overall, these results constitute an important step towards practical real-time learning control with continuous action.

Domaines: Apprentissage [cs.LG]

Thomas Degris, Patrick M. Pilarski, Richard S. Sutton. Model-Free Reinforcement Learning with Continuous Action in Practice. American Control Conference, Jun 2012, Montreal, Canada. ⟨hal-00764281⟩