34 citations · 46 across the 14 of their papers we have counts for
8 papers · 1 filter
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…
Improving Interactive In-Context Learning from Natural Language Feedback
Martin Klissarov, Jonathan Cook, Diego Antognini +5
Adapting one's thought process based on corrective feedback is an essential ability in human learning, particularly in collaborative settings. In contrast, the current large langua…
Position: Introspective Experience from Conversational Environments as a Path to Better Learning
Claudiu Cristian Musat, Jackson Tolins, Diego Antognini +3
Current approaches to AI training treat reasoning as an emergent property of scale. We argue instead that robust reasoning emerges from linguistic self-reflection, itself internali…
Discovering Temporal Structure: An Overview of Hierarchical Reinforcement Learning
Martin Klissarov, Akhil Bagaria, Ziyan Luo +3
Developing agents capable of exploring, planning and learning in complex open-ended environments is a grand challenge in artificial intelligence (AI). Hierarchical reinforcement le…
MaestroMotif: Skill Design from Artificial Intelligence Feedback
Martin Klissarov, Mikael Henaff, Roberta Raileanu +7
Describing skills in natural language has the potential to provide an accessible way to inject human knowledge about decision-making into an AI system. We present MaestroMotif, a m…
On the Modeling Capabilities of Large Language Models for Sequential Decision Making
Martin Klissarov, Devon Hjelm, Alexander Toshev +1
Large pretrained models are showing increasingly better performance in reasoning and planning tasks across different modalities, opening the possibility to leverage them for comple…
Motif: Intrinsic Motivation from Artificial Intelligence Feedback
Martin Klissarov, Pierluca D'Oro, Shagun Sodhani +5
Exploring rich environments and evaluating one's actions without prior knowledge is immensely challenging. In this paper, we propose Motif, a general method to interface such prior…
When Waiting is not an Option : Learning Options with a Deliberation Cost
Jean Harb, Pierre-Luc Bacon, Martin Klissarov +1
Recent work has shown that temporally extended actions (options) can be learned fully end-to-end as opposed to being specified in advance. While the problem of "how" to learn optio…