Offline reinforcement learning (RL) algorithms are applied to learn performant, well-generalizing policies when provided with a static dataset of interactions. Many recent approaches to offline RL have seen substantial success, but with one key caveat: they demand substantial per-dataset hyperparameter tuning to achieve reported performance, which requires policy rollouts in the environment to evaluate; this can rapidly become cumbersome. Furthermore, substantial tuning requirements can hamper the adoption of these algorithms in practical domains. In this paper, we present TD3 with Behavioral Supervisor Tuning (TD3-BST), an algorithm that trains an uncertainty model and uses it to guide the policy to select actions within the dataset support. TD3-BST can learn more effective policies from offline datasets compared to previous methods and achieves the best performance across challenging benchmarks without requiring per-dataset tuning.

As a result, the development network, which generalizes the unnormalized of methods to quantify neural network uncertainty is an Gaussian densities to have modes of highdimensional increasingly important subject in deep learning research submanifolds instead of just discrete (Amodei et al., 2016). In particular, neural networks tend to points. Fitting the Morse neural network via a KLdivergence produce confidently wrong predictions when presented with loss yields 1) a (unnormalized) generative Out-Of-Distribution (OOD) inputs, that is, inputs that are density, 2) an OOD detector, 3) a calibration far away from the data distribution with which the model temperature, 4) a generative sampler, along was trained (Murphy, 2023; Nagarajan et al., 2021; Liu with in the supervised case 5) a distance awareclassifier.