Workshop on Reinforcement Learning Beyond Rewards

Reinforcement Learning Conference (RLC) 2024

August 9, 2024

@RLBRew_2024 · #RLBRew_2024

Reinforcement learning has been widely successful in solving particular tasks defined with a reward function - from superhuman Go playing to magnetic confinement for plasma control. On the other hand, creating a generalist RL agent poses the unresolved question of what agent can learn not just from reward-defined environments, but from the often substantial quantity of reward-free interactions with the environment. This question has been explored recently and has taken diverse forms—learning representations that are action-free, causal, predictive, and contrastive; learning from large-scale action-free datasets; learning exploration using intrinsic reward and skill discovery; learning policies that are arbitrary goal-reaching, language-conditioned, policies optimal for distribution of reward function, or even optimal for all reward functions; learning intent from datasets using a variety of learning signals like preferences, rankings, expert, and human cues; learning imitative foundational action models. The RLBrew workshop focuses on this setting of reward-free RL. Considering the wide variety of possibilities for RL beyond rewards, we aim to bring a set of diverse opinions to the table to spark discussion about the right questions and novel tools to introduce new capabilities for RL agents in the reward-free setting.


Caleb Chuck
UT Austin
Siddhant Agarwal
UT Austin
Fan Feng
City University of Hong Kong
Yuchen Cui
Harshit Sikchi
UT Austin
Akanksha Saran
Sony AI
Joey Hejna
Gokul Swamy
Carnegie Mellon University
Roberta Raileanu
FAIR, Meta

To contact the organizers, please send an email to or @ us on X/Twitter at @RLBRew_2024