Learning efficiently from small amounts of data has long been the focus of model-based reinforcement learning, both for the online case when interacting with the environment, and the offline case when learning from a fixed dataset. However, to date no single unified algorithm could demonstrate state-of-the-art results for both settings.In this work, we describe the Reanalyse algorithm, which uses model-based policy and value improvement operators to compute improved training targets for existing data points, allowing for efficient learning at data budgets varying by several orders of magnitude. We further show that Reanalyse can also be used to learn completely without environment interactions, as in the case of Offline Reinforcement Learning (Offline RL). Combining Reanalyse with the MuZero algorithm, we introduce MuZero Unplugged, a single unified algorithm for any data budget, including Offline RL. In contrast to previous work, our algorithm requires no special adaptations for the off-policy or Offline RL settings.

Learning efficiently from small amounts of data has long been the focus of model-based reinforcement learning, both for the online case when interacting with the environment, and the offline case when learning from a fixed dataset. However, to date no single unified algorithm could demonstrate state-of-the-art results for both settings.In this work, we describe the Reanalyse algorithm, which uses model-based policy and value improvement operators to compute improved training targets for existing data points, allowing for efficient learning at data budgets varying by several orders of magnitude. We further show that Reanalyse can also be used to learn completely without environment interactions, as in the case of Offline Reinforcement Learning (Offline RL). Combining Reanalyse with the MuZero algorithm, we introduce MuZero Unplugged, a single unified algorithm for any data budget, including Offline RL. In contrast to previous work, our algorithm requires no special adaptations for the off-policy or Offline RL settings.

The field of antibody-based therapeutics has grown significantly in recent years, with targeted antibodies emerging as a potentially effective approach to personalized therapies. Such therapies could be particularly beneficial for complex, highly individual diseases such as cancer. However, progress in this field is often constrained by the extensive search space of amino acid sequences that form the foundation of antibody design. In this study, we introduce a novel reinforcement learning method specifically tailored to address the unique challenges of this domain. We demonstrate that our method can learn the design of high-affinity antibodies against multiple targets in silico, utilizing either online interaction or offline datasets. To the best of our knowledge, our approach is the first of its kind and outperforms existing methods on all tested antigens in the Absolut!