Exploration and Online Transfer with Behavioral Foundation Models
Zero-shot Transfer in Reinforcement Learning (RL) aims to train an agent that can generate optimal policies for any reward function, without additional learning at transfer time, while training only on reward-free trajectories. For their generality over tasks, such models are sometimes called ``Behavioral Foundation Models'' (BFMs). While they have shown strong performances and improvements in recent years, the current framework and algorithms still assume that, during the transfer phase, the agent is informed offline about the reward (the task to solve) through a dataset of state-reward pairs, which it uses to pick the best policy to deploy. However, in practice if the reward is a black-...
| Authors | Louis Bagot, Mathieu Lefort, Laëtitia Matignon |
|---|---|
| Primary category | cs.AI |
| Published | 29 June 2026 |
| Last updated at source | 30 June 2026 |
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