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This research identifies representation learning as the critical factor for scaling multitask reinforcement learning, demonstrating that combining predictive representations with high-capacity value function approximation outperforms complex planning-based methods. By utilizing a simple model-free architecture, the proposed approach significantly reduces computational overhead and improves wall-clock efficiency across diverse continuous control tasks. The findings suggest that predictive objectives are essential for performance, offering a more streamlined path to scalable deep RL without relying on intricate training pipelines.
Representation Learning Enables Scalable Multitask Deep Reinforcement Learning
arXiv:2606.05555v1 Announce Type: cross Abstract: Scaling reinforcement learning (RL) to diverse multitask settings remains a central challenge. While recent advances in model-based RL achieve strong performance, they rely on planning and complex training pipelines, making it unclear which components are essential for scalability. We revisit this question and argue that the primary driver of scalable multitask RL is not model-based control, but \emph{representation learning}. In particular, we show that combining predictive, model-based representations with high-capacity value function approximation is sufficient to achieve strong performance, even without planning. We evaluate a simple model-free algorithm, MR.Q, coupled with auxiliary predictive objectives into a scalable actor-critic architecture. This approach outperforms a recent world-model-based method and a range of deep RL baselines across a diverse suite of multitask continuous control tasks, while significantly reducing computational overhead and improving wall-clock efficiency. We observe consistent improvements with increased model capacity and show through ablations that predictive representation learning is critical for performance.
Abstract:Scaling reinforcement learning (RL) to diverse multitask settings remains a central challenge. While recent advances in model-based RL achieve strong performance, they rely on planning and complex training pipelines, making it unclear which components are essential for scalability. We revisit this question and argue that the primary driver of scalable multitask RL is not model-based control, but \emph{representation learning}. In particular, we show that combining predictive, model-based representations with high-capacity value function approximation is sufficient to achieve strong performance, even without planning. We evaluate a simple model-free algorithm, MR.Q, coupled with auxiliary predictive objectives into a scalable actor-critic architecture. This approach outperforms a recent world-model-based method and a range of deep RL baselines across a diverse suite of multitask continuous control tasks, while significantly reducing computational overhead and improving wall-clock efficiency. We observe consistent improvements with increased model capacity and show through ablations that predictive representation learning is critical for performance.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2606.05555 [cs.LG]
(or arXiv:2606.05555v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.05555 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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