Timezone: »

 
Poster
Projections for Approximate Policy Iteration Algorithms
Riad Akrour · Joni Pajarinen · Jan Peters · Gerhard Neumann

Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #34
in Posters Thu »

Approximate policy iteration is a class of reinforcement learning (RL) algorithms where the policy is encoded using a function approximator and which has been especially prominent in RL with continuous action spaces. In this class of RL algorithms, ensuring increase of the policy return during policy update often requires to constrain the change in action distribution. Several approximations exist in the literature to solve this constrained policy update problem. In this paper, we propose to improve over such solutions by introducing a set of projections that transform the constrained problem into an unconstrained one which is then solved by standard gradient descent. Using these projections, we empirically demonstrate that our approach can improve the policy update solution and the control over exploration of existing approximate policy iteration algorithms.

Keywords: Deep Reinforcement Learning · Robotics · Theory and Algorithms

Author Information

Riad Akrour (TU Darmstadt)
Joni Pajarinen (TU Darmstadt)
Jan Peters (TU Darmstadt + Max Planck Institute for Intelligent Systems)
Gerhard Neumann (University of Lincoln)

Related Events (a corresponding poster, oral, or spotlight)

More from the Same Authors