7 papers · 1 filter
Utility-Constrained Policy Optimization
Mehrdad Moghimi, Bernardo Avila Pires
Constrained MDPs (CMDPs) are a widely adopted framework for incorporating safety into RL agents; however, the framework does not support risk-sensitive constraints. This can be pro…
Optimizing Return Distributions with Distributional Dynamic Programming
Bernardo Ãvila Pires, Mark Rowland, Diana Borsa +6
We introduce distributional dynamic programming (DP) methods for optimizing statistical functionals of the return distribution, with standard reinforcement learning as a special ca…
Representation Learning via Non-Contrastive Mutual Information
Zhaohan Daniel Guo, Bernardo Avila Pires, Khimya Khetarpal +2
Labeling data is often very time consuming and expensive, leaving us with a majority of unlabeled data. Self-supervised representation learning methods such as SimCLR (Chen et al.,…
A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning
Khimya Khetarpal, Zhaohan Daniel Guo, Bernardo Avila Pires +7
Learning a good representation is a crucial challenge for Reinforcement Learning (RL) agents. Self-predictive learning provides means to jointly learn a latent representation and d…
Offline Regularised Reinforcement Learning for Large Language Models Alignment
Pierre Harvey Richemond, Yunhao Tang, Daniel Guo +15
The dominant framework for alignment of large language models (LLM), whether through reinforcement learning from human feedback or direct preference optimisation, is to learn from…
Generalized Preference Optimization: A Unified Approach to Offline Alignment
Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng +7
Offline preference optimization allows fine-tuning large models directly from offline data, and has proved effective in recent alignment practices. We propose generalized preferenc…