most citedoIRL: Robust Adversarial Inverse Reinforcement Learning with Temporally Extended Actions

1 citations · 1 across the 2 of their papers we have counts for

collaborators

2 papers

cs.LG2020

A Study of Policy Gradient on a Class of Exactly Solvable Models

Gavin McCracken, Colin Daniels, Rosie Zhao +3

Policy gradient methods are extensively used in reinforcement learning as a way to optimize expected return. In this paper, we explore the evolution of the policy parameters, for a…

cs.LG2020★ 1 cited

oIRL: Robust Adversarial Inverse Reinforcement Learning with Temporally Extended Actions

David Venuto, Jhelum Chakravorty, Leonard Boussioux +3

Explicit engineering of reward functions for given environments has been a major hindrance to reinforcement learning methods. While Inverse Reinforcement Learning (IRL) is a soluti…