Title: Long-Horizon Model-Based Offline Reinforcement Learning Without Conservatism
Primary area: reinforcement learning
Keywords: Offline RL, Model-based RL, Bayesian RL, Partially Observable RL, Deep RL

Abstract: Popular offline reinforcement learning (RL) methods rely on *conservatism*, either by penalizing out-of-dataset actions or by restricting planning horizons. In this work, we question the universality of this principle and instead revisit a complementary one: a *Bayesian* perspective. Rather than enforcing conservatism, the Bayesian approach tackles epistemic uncertainty in offline data by modeling a posterior distribution over plausible world models and training a history-dependent agent to maximize expected rewards, enabling test-time generalization. We first illustrate, in a bandit setting, that Bayesianism excels on low-quality datasets where conservatism fails. We then scale the principle to realistic tasks, identifying key design choices, such as layer normalization in the world model and adaptive long-horizon planning, that mitigate compounding error and value overestimation. These yield our practical algorithm, Neubay, grounded in the *neu*tral *Bay*esian principle. On D4RL and NeoRL benchmarks, Neubay generally matches or surpasses leading conservative algorithms, achieving new state-of-the-art on 7 datasets. Notably, it succeeds with planning horizons of several hundred steps, challenging common belief. Finally, we characterize when Neubay is preferable to conservatism, laying the foundation for a new direction in offline and model-based RL.

TL;DR: We present a Bayesian-principled offline RL algorithm that succeeds with adaptive long-horizon planning, without conservatism.