Off-Policy Shaping Ensembles in Reinforcement Learning

Understand

Recent advances of gradient temporal-difference methods allow to learn off-policy multiple value functions in parallel with- out sacrificing convergence guarantees or computational efficiency.

• This opens up new possibilities for sound ensemble techniques in reinforcement learning.
• In this work we propose learning an ensemble of policies related through potential-based shaping rewards.
• The ensemble induces a combination policy by using a voting mechanism on its components.