2 papers
cs.AI2021
Dynamic probabilistic logic models for effective abstractions in RL
Harsha Kokel, Arjun Manoharan, Sriraam Natarajan +2
State abstraction enables sample-efficient learning and better task transfer in complex reinforcement learning environments. Recently, we proposed RePReL (Kokel et al. 2021), a hie…
cs.LG2019
Option Encoder: A Framework for Discovering a Policy Basis in Reinforcement Learning
Arjun Manoharan, Rahul Ramesh, Balaraman Ravindran
Option discovery and skill acquisition frameworks are integral to the functioning of a Hierarchically organized Reinforcement learning agent. However, such techniques often yield a…