83 citations · 264 across the 14 of their papers we have counts for
14 papers
Offline Materials Optimization with CliqueFlowmer
Jakub Grudzien Kuba, Benjamin Kurt Miller, Sergey Levine +1
Recent advances in deep learning inspired neural network-based approaches to computational materials discovery (CMD). A plethora of problems in this field involve finding materials…
Language Self-Play For Data-Free Training
Jakub Grudzien Kuba, Mengting Gu, Qi Ma +3
Large language models (LLMs) have advanced rapidly in recent years, driven by scale, abundant high-quality training data, and reinforcement learning. Yet this progress faces a fund…
Cliqueformer: Model-Based Optimization with Structured Transformers
Jakub Grudzien Kuba, Pieter Abbeel, Sergey Levine
Large neural networks excel at prediction tasks, but their application to design problems, such as protein engineering or materials discovery, requires solving offline model-based…
Functional Graphical Models: Structure Enables Offline Data-Driven Optimization
Jakub Grudzien Kuba, Masatoshi Uehara, Pieter Abbeel +1
While machine learning models are typically trained to solve prediction problems, we might often want to use them for optimization problems. For example, given a dataset of protein…
IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies
Philippe Hansen-Estruch, Ilya Kostrikov, Michael Janner +2
Effective offline RL methods require properly handling out-of-distribution actions. Implicit Q-learning (IQL) addresses this by training a Q-function using only dataset actions thr…
Heterogeneous-Agent Reinforcement Learning
Yifan Zhong, Jakub Grudzien Kuba, Xidong Feng +3
The necessity for cooperation among intelligent machines has popularised cooperative multi-agent reinforcement learning (MARL) in AI research. However, many research endeavours hea…
Discovered Policy Optimisation
Chris Lu, Jakub Grudzien Kuba, Alistair Letcher +3
Tremendous progress has been made in reinforcement learning (RL) over the past decade. Most of these advancements came through the continual development of new algorithms, which we…
Heterogeneous-Agent Mirror Learning: A Continuum of Solutions to Cooperative MARL
Jakub Grudzien Kuba, Xidong Feng, Shiyao Ding +3
The necessity for cooperation among intelligent machines has popularised cooperative multi-agent reinforcement learning (MARL) in the artificial intelligence (AI) research communit…
Multi-Agent Reinforcement Learning is a Sequence Modeling Problem
Muning Wen, Jakub Grudzien Kuba, Runji Lin +4
Large sequence model (SM) such as GPT series and BERT has displayed outstanding performance and generalization capabilities on vision, language, and recently reinforcement learning…
Understanding Value Decomposition Algorithms in Deep Cooperative Multi-Agent Reinforcement Learning
Zehao Dou, Jakub Grudzien Kuba, Yaodong Yang
Value function decomposition is becoming a popular rule of thumb for scaling up multi-agent reinforcement learning (MARL) in cooperative games. For such a decomposition rule to hol…
Mirror Learning: A Unifying Framework of Policy Optimisation
Jakub Grudzien Kuba, Christian Schroeder de Witt, Jakob Foerster
Modern deep reinforcement learning (RL) algorithms are motivated by either the generalised policy iteration (GPI) or trust-region learning (TRL) frameworks. However, algorithms tha…
Multi-Agent Constrained Policy Optimisation
Shangding Gu, Jakub Grudzien Kuba, Munning Wen +6
Developing reinforcement learning algorithms that satisfy safety constraints is becoming increasingly important in real-world applications. In multi-agent reinforcement learning (M…
Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning
Jakub Grudzien Kuba, Ruiqing Chen, Muning Wen +4
Trust region methods rigorously enabled reinforcement learning (RL) agents to learn monotonically improving policies, leading to superior performance on a variety of tasks. Unfortu…
Settling the Variance of Multi-Agent Policy Gradients
Jakub Grudzien Kuba, Muning Wen, Yaodong Yang +5
Policy gradient (PG) methods are popular reinforcement learning (RL) methods where a baseline is often applied to reduce the variance of gradient estimates. In multi-agent RL (MARL…