no code implementations • 1 Sep 2021 • Susan Amin, Maziar Gomrokchi, Harsh Satija, Herke van Hoof, Doina Precup
Exploration is an essential component of reinforcement learning algorithms, where agents need to learn how to predict and control unknown and often stochastic environments.
no code implementations • NeurIPS 2021 • Harsh Satija, Philip S. Thomas, Joelle Pineau, Romain Laroche
We study the problem of Safe Policy Improvement (SPI) under constraints in the offline Reinforcement Learning (RL) setting.
1 code implementation • 26 Dec 2020 • Susan Amin, Maziar Gomrokchi, Hossein Aboutalebi, Harsh Satija, Doina Precup
A major challenge in reinforcement learning is the design of exploration strategies, especially for environments with sparse reward structures and continuous state and action spaces.
no code implementations • ICML 2020 • Harsh Satija, Philip Amortila, Joelle Pineau
In standard RL, the agent is incentivized to explore any behavior as long as it maximizes rewards, but in the real world, undesired behavior can damage either the system or the agent in a way that breaks the learning process itself.
1 code implementation • 6 Jun 2018 • Ahmed Touati, Harsh Satija, Joshua Romoff, Joelle Pineau, Pascal Vincent
In particular, we augment DQN and DDPG with multiplicative normalizing flows in order to track a rich approximate posterior distribution over the parameters of the value function.
1 code implementation • 27 Apr 2018 • Amy Zhang, Harsh Satija, Joelle Pineau
Current reinforcement learning (RL) methods can successfully learn single tasks but often generalize poorly to modest perturbations in task domain or training procedure.