34 citations · 46 across the 14 of their papers we have counts for
6 papers · 1 filter
Code as Reward: Empowering Reinforcement Learning with VLMs
David Venuto, Sami Nur Islam, Martin Klissarov +3
Pre-trained Vision-Language Models (VLMs) are able to understand visual concepts, describe and decompose complex tasks into sub-tasks, and provide feedback on task completion. In t…
Deep Laplacian-based Options for Temporally-Extended Exploration
Martin Klissarov, Marlos C. Machado
Selecting exploratory actions that generate a rich stream of experience for better learning is a fundamental challenge in reinforcement learning (RL). An approach to tackle this pr…
Flexible Option Learning
Martin Klissarov, Doina Precup
Temporal abstraction in reinforcement learning (RL), offers the promise of improving generalization and knowledge transfer in complex environments, by propagating information more…
Reward Propagation Using Graph Convolutional Networks
Martin Klissarov, Doina Precup
Potential-based reward shaping provides an approach for designing good reward functions, with the purpose of speeding up learning. However, automatically finding potential function…
Options of Interest: Temporal Abstraction with Interest Functions
Khimya Khetarpal, Martin Klissarov, Maxime Chevalier-Boisvert +2
Temporal abstraction refers to the ability of an agent to use behaviours of controllers which act for a limited, variable amount of time. The options framework describes such behav…
Learnings Options End-to-End for Continuous Action Tasks
Martin Klissarov, Pierre-Luc Bacon, Jean Harb +1
We present new results on learning temporally extended actions for continuoustasks, using the options framework (Suttonet al.[1999b], Precup [2000]). In orderto achieve this goal w…