20 citations · 33 across the 11 of their papers we have counts for
12 papers
Up and Down the Abstraction Ladder: Code-Based Skills for Language Agents
Bartłomiej Cupiał, Jens Tuyls, Maciej Wołczyk +5
Language agents struggle to act and learn in environments that require long sequences of low-level actions. Code-based abstractions can make these agents more productive by letting…
A game theory for foundation models shows new paths to rational cooperation through similarity inference
Alexander Meulemans, Maciej Wołczyk, Marissa A. Weis +11
As autonomous agents powered by foundation models are increasingly integrated into social and economic systems, understanding the principles governing their collective behavior is…
Multi-agent cooperation through in-context co-player inference
Marissa A. Weis, Maciej Wołczyk, Rajai Nasser +4
Achieving cooperation among self-interested agents remains a fundamental challenge in multi-agent reinforcement learning. Recent work showed that mutual cooperation can be induced…
Emergent temporal abstractions in autoregressive models enable hierarchical reinforcement learning
Seijin Kobayashi, Yanick Schimpf, Maximilian Schlegel +12
Large-scale autoregressive models pretrained on next-token prediction and finetuned with reinforcement learning (RL) have achieved unprecedented success on many problem domains. Du…
Embedded Universal Predictive Intelligence: a coherent framework for multi-agent learning
Alexander Meulemans, Rajai Nasser, Maciej Wołczyk +10
The standard theory of model-free reinforcement learning assumes that the environment dynamics are stationary and that agents are decoupled from their environment, such that polici…
BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games
Davide Paglieri, Bartłomiej Cupiał, Samuel Coward +10
Large Language Models (LLMs) and Vision Language Models (VLMs) possess extensive knowledge and exhibit promising reasoning abilities, however, they still struggle to perform well i…
State Soup: In-Context Skill Learning, Retrieval and Mixing
Maciej Pióro, Maciej Wołczyk, Razvan Pascanu +2
A new breed of gated-linear recurrent neural networks has reached state-of-the-art performance on a range of sequence modeling problems. Such models naturally handle long sequences…
Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem
Maciej Wołczyk, Bartłomiej Cupiał, Mateusz Ostaszewski +5
Fine-tuning is a widespread technique that allows practitioners to transfer pre-trained capabilities, as recently showcased by the successful applications of foundation models. How…
Discovering modular solutions that generalize compositionally
Simon Schug, Seijin Kobayashi, Yassir Akram +6
Many complex tasks can be decomposed into simpler, independent parts. Discovering such underlying compositional structure has the potential to enable compositional generalization.…
The Effectiveness of World Models for Continual Reinforcement Learning
Samuel Kessler, Mateusz Ostaszewski, Michał Bortkiewicz +5
World models power some of the most efficient reinforcement learning algorithms. In this work, we showcase that they can be harnessed for continual learning - a situation when the…
Disentangling Transfer in Continual Reinforcement Learning
Maciej Wołczyk, Michał Zając, Razvan Pascanu +2
The ability of continual learning systems to transfer knowledge from previously seen tasks in order to maximize performance on new tasks is a significant challenge for the field, l…
Continual World: A Robotic Benchmark For Continual Reinforcement Learning
Maciej Wołczyk, Michał Zając, Razvan Pascanu +2
Continual learning (CL) -- the ability to continuously learn, building on previously acquired knowledge -- is a natural requirement for long-lived autonomous reinforcement learning…