Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control

Glen Berseth, Cheng Xie, Paul Cernek, Michiel Van de Panne

Abstract:

Deep reinforcement learning has demonstrated increasing capabilities for continuous control problems,
including agents that can move with skill and agility through their environment.
An open problem in this setting is that of developing good strategies for integrating or merging policies
for multiple skills, where each individual skill is a specialist in a specific skill and its associated state distribution.
We extend policy distillation methods to the continuous action setting and leverage this technique to combine \expert policies,
as evaluated in the domain of simulated bipedal locomotion across different classes of terrain.
We also introduce an input injection method for augmenting an existing policy network to exploit new input features.
Lastly, our method uses transfer learning to assist in the efficient acquisition of new skills.
The combination of these methods allows a policy to be incrementally augmented with new skills.
We compare our progressive learning and integration via distillation (PLAID) method
against three alternative baselines.

TL;DR: A continual learning method that uses distillation to combine expert policies and transfer learning to accelerate learning new skills.

Keywords: Reinforcement Learning, Distillation, Transfer Learning, Continual Learning