… We will cover a variety of offline reinforcement learning methods studied in the literature.
For each one, we will discuss the conceptual challenges, and initial steps taken to mitigate …

… Offline RL is a paradigm that learns exclusively from static … Effective offline RL algorithms
have a much wider range of … a unifying taxonomy to classify offline RL methods. Furthermore, …

… reinforcement learning (RL) using a fixed offline dataset of logged interactions is an important
consideration in real world applications. This paper studies offline … in the offline setting, we …

… outperforms existing offline RL methods, often learning policies that attain 2-5 times higher
final return, especially when learning from complex and multi-modal data distributions. …

… in offline RL. In this paper, we ask: can we make a deep RL algorithm work offline with
minimal changes? We find that we can match the performance of state-of-the-art offline RL …

… offline RL, which allows for data driven policy learning using pre-collected datasets. The ability
to train policies offline can … online learning. Since the dataset has already been collected, …

… for data efficient learning and offline RL, leading to MuZero Unplugged. We demonstrate
its effectiveness for the online case through results on Atari and for the offline case through …

… -scale language modeling instead of those normally associated with control, we find that this
approach is effective in imitation learning, goal-reaching, and offline reinforcement learning. …

… In this section, we discuss how our approach is related to prior work on offline
reinforcement learning. In particular, we discuss connections to BCQ Fujimoto et al. (2019). …

… fails to learn a good policy (eg, in Walker2d) in the offline setting. Using k = 4 has a small
advantage compared to k = 2 except in Hopper. Both k = 2 and k = 4 significantly improve …