Search Results for author: Usman Anwar

Found 7 papers, 3 papers with code

Reward Model Ensembles Help Mitigate Overoptimization

1 code implementation4 Oct 2023 Thomas Coste, Usman Anwar, Robert Kirk, David Krueger

Gao et al. (2023) studied this phenomenon in a synthetic human feedback setup with a significantly larger "gold" reward model acting as the true reward (instead of humans) and showed that overoptimization remains a persistent problem regardless of the size of the proxy reward model and training data used.

Model Optimization

Domain Generalization for Robust Model-Based Offline Reinforcement Learning

no code implementations27 Nov 2022 Alan Clark, Shoaib Ahmed Siddiqui, Robert Kirk, Usman Anwar, Stephen Chung, David Krueger

Existing offline reinforcement learning (RL) algorithms typically assume that training data is either: 1) generated by a known policy, or 2) of entirely unknown origin.

Domain Generalization Offline RL +2

Constrained Reinforcement Learning With Learned Constraints

no code implementations1 Jan 2021 Shehryar Malik, Usman Anwar, Alireza Aghasi, Ali Ahmed

In this work, given a reward function and a set of demonstrations from an expert that maximizes this reward function while respecting \textit{unknown} constraints, we propose a framework to learn the most likely constraints that the expert respects.

reinforcement-learning Reinforcement Learning (RL)

Inverse Constrained Reinforcement Learning

1 code implementation19 Nov 2020 Usman Anwar, Shehryar Malik, Alireza Aghasi, Ali Ahmed

However, for the real world deployment of reinforcement learning (RL), it is critical that RL agents are aware of these constraints, so that they can act safely.

reinforcement-learning Reinforcement Learning (RL)

Learning To Solve Differential Equations Across Initial Conditions

no code implementations ICLR Workshop DeepDiffEq 2019 Shehryar Malik, Usman Anwar, Ali Ahmed, Alireza Aghasi

Recently, there has been a lot of interest in using neural networks for solving partial differential equations.

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