oIRL: Robust Adversarial Inverse Reinforcement Learning with Temporally Extended Actions

no code implementations • 20 Feb 2020 • David Venuto, Jhelum Chakravorty, Leonard Boussioux, Junhao Wang, Gavin McCracken, Doina Precup

Explicit engineering of reward functions for given environments has been a major hindrance to reinforcement learning methods.

Continuous Control reinforcement-learning +2

Option-Critic in Cooperative Multi-agent Systems

1 code implementation • 28 Nov 2019 • Jhelum Chakravorty, Nadeem Ward, Julien Roy, Maxime Chevalier-Boisvert, Sumana Basu, Andrei Lupu, Doina Precup

In this paper, we investigate learning temporal abstractions in cooperative multi-agent systems, using the options framework (Sutton et al, 1999).

Avoidance Learning Using Observational Reinforcement Learning

1 code implementation • 24 Sep 2019 • David Venuto, Leonard Boussioux, Junhao Wang, Rola Dali, Jhelum Chakravorty, Yoshua Bengio, Doina Precup

We define avoidance learning as the process of optimizing the agent's reward while avoiding dangerous behaviors given by a demonstrator.

Imitation Learning reinforcement-learning +1

Sufficient conditions for the value function and optimal strategy to be even and quasi-convex

no code implementations • 31 Mar 2017 • Jhelum Chakravorty, Aditya Mahajan

Sufficient conditions are identified under which the value function and the optimal strategy of a Markov decision process (MDP) are even and quasi-convex in the state.

Optimization and Control