Sahand Rezaei-Shoshtari

Fairness in Reinforcement Learning with Bisimulation Metrics

Hanna Yurchyk

Scott Fujimoto

Doina Precup

Ensuring long-term fairness is crucial when developing automated decision making systems, specifically in dynamic and sequential environment… (voir plus)s. By maximizing their reward without consideration of fairness, AI agents can introduce disparities in their treatment of groups or individuals. In this paper, we establish the connection between bisimulation metrics and group fairness in reinforcement learning. We propose a novel approach that leverages bisimulation metrics to learn reward functions and observation dynamics, ensuring that learners treat groups fairly while reflecting the original problem. We demonstrate the effectiveness of our method in addressing disparities in sequential decision making problems through empirical evaluation on a standard fairness benchmark consisting of lending and college admission scenarios.

2024-12-22

ArXiv (prépublication)

Policy Gradient Methods in the Presence of Symmetries and State Abstractions

Prakash Panangaden

Rosie Zhao

Doina Precup

Hypernetworks for Zero-shot Transfer in Reinforcement Learning

Charlotte Morissette

Francois Hogan

Gregory Dudek

In this paper, hypernetworks are trained to generate behaviors across a range of unseen task conditions, via a novel TD-based training objec… (voir plus)tive and data from a set of near-optimal RL solutions for training tasks. This work relates to meta RL, contextual RL, and transfer learning, with a particular focus on zero-shot performance at test time, enabled by knowledge of the task parameters (also known as context). Our technical approach is based upon viewing each RL algorithm as a mapping from the MDP specifics to the near-optimal value function and policy and seek to approximate it with a hypernetwork that can generate near-optimal value functions and policies, given the parameters of the MDP. We show that, under certain conditions, this mapping can be considered as a supervised learning problem. We empirically evaluate the effectiveness of our method for zero-shot transfer to new reward and transition dynamics on a series of continuous control tasks from DeepMind Control Suite. Our method demonstrates significant improvements over baselines from multitask and meta RL approaches.

2023-06-26

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

doi.org

Continuous MDP Homomorphisms and Homomorphic Policy Gradient

Rosie Zhao

Learning Intuitive Physics with Multimodal Generative Models

Francois Hogan

M. Jenkin

Gregory Dudek

Predicting the future interaction of objects when they come into contact with their environment is key for autonomous agents to take intelli… (voir plus)gent and anticipatory actions. This paper presents a perception framework that fuses visual and tactile feedback to make predictions about the expected motion of objects in dynamic scenes. Visual information captures object properties such as 3D shape and location, while tactile information provides critical cues about interaction forces and resulting object motion when it makes contact with the environment. Utilizing a novel See-Through-your-Skin (STS) sensor that provides high resolution multimodal sensing of contact surfaces, our system captures both the visual appearance and the tactile properties of objects. We interpret the dual stream signals from the sensor using a Multimodal Variational Autoencoder (MVAE), allowing us to capture both modalities of contacting objects and to develop a mapping from visual to tactile interaction and vice-versa. Additionally, the perceptual system can be used to infer the outcome of future physical interactions, which we validate through simulated and real-world experiments in which the resting state of an object is predicted from given initial conditions.

2021-05-18

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

doi.org

Seeing Through Your Skin: A Novel Visuo-Tactile Sensor for Robotic Manipulation

Francois Hogan

M. Jenkin

Yashveer Girdhar