Fairness Incentives in Response to Unfair Dynamic Pricing

no code implementations • 22 Apr 2024 • Jesse Thibodeau, Hadi Nekoei, Afaf Taïk, Janarthanan Rajendran, Golnoosh Farnadi

We find that, upon deploying a learned tax and redistribution policy, social welfare improves on that of the fairness-agnostic baseline, and approaches that of the analytically optimal fairness-aware baseline for the multi-armed and contextual bandit settings, and surpassing it by 13. 19% in the full RL setting.

Fairness Reinforcement Learning (RL)

Towards Few-shot Coordination: Revisiting Ad-hoc Teamplay Challenge In the Game of Hanabi

1 code implementation • 20 Aug 2023 • Hadi Nekoei, Xutong Zhao, Janarthanan Rajendran, Miao Liu, Sarath Chandar

In this work, we show empirically that state-of-the-art ZSC algorithms have poor performance when paired with agents trained with different learning methods, and they require millions of interaction samples to adapt to these new partners.

Game of Hanabi Multi-agent Reinforcement Learning +1

Dealing With Non-stationarity in Decentralized Cooperative Multi-Agent Deep Reinforcement Learning via Multi-Timescale Learning

no code implementations • 6 Feb 2023 • Hadi Nekoei, Akilesh Badrinaaraayanan, Amit Sinha, Mohammad Amini, Janarthanan Rajendran, Aditya Mahajan, Sarath Chandar

In our proposed method, when one agent updates its policy, other agents are allowed to update their policies as well, but at a slower rate.

Multi-Agent Reinforcement Learning for Fast-Timescale Demand Response of Residential Loads

no code implementations • 6 Jan 2023 • Vincent Mai, Philippe Maisonneuve, Tianyu Zhang, Hadi Nekoei, Liam Paull, Antoine Lesage-Landry

To integrate high amounts of renewable energy resources, electrical power grids must be able to cope with high amplitude, fast timescale variations in power generation.

Multi-agent Reinforcement Learning reinforcement-learning +1

Continuous Coordination As a Realistic Scenario for Lifelong Learning

2 code implementations • 4 Mar 2021 • Hadi Nekoei, Akilesh Badrinaaraayanan, Aaron Courville, Sarath Chandar

Its large strategy space makes it a desirable environment for lifelong RL tasks.

Continual Learning Reinforcement Learning (RL)

DEUP: Direct Epistemic Uncertainty Prediction

1 code implementation • 16 Feb 2021 • Salem Lahlou, Moksh Jain, Hadi Nekoei, Victor Ion Butoi, Paul Bertin, Jarrid Rector-Brooks, Maksym Korablyov, Yoshua Bengio

Epistemic Uncertainty is a measure of the lack of knowledge of a learner which diminishes with more evidence.

Active Learning Image Classification +2

Sampling Algorithms, from Survey Sampling to Monte Carlo Methods: Tutorial and Literature Review

no code implementations • 2 Nov 2020 • Benyamin Ghojogh, Hadi Nekoei, Aydin Ghojogh, Fakhri Karray, Mark Crowley

This paper is a tutorial and literature review on sampling algorithms.

Survey Sampling

The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning

2 code implementations • NeurIPS 2020 • Harm van Seijen, Hadi Nekoei, Evan Racah, Sarath Chandar

For example, the common single-task sample-efficiency metric conflates improvements due to model-based learning with various other aspects, such as representation learning, making it difficult to assess true progress on model-based RL.

Model-based Reinforcement Learning Reinforcement Learning (RL) +1