May 6, 2024 · In this paper, we introduce a memory technique, (Prioritized) Trajectory Replay (TR/PTR), to facilitate trajectory data storage and sampling.
Oct 12, 2024 · In this paper, we introduce a memory technique, (Prioritized) Trajectory Replay (TR/PTR), to facilitate trajectory data storage and sampling.
May 6, 2024 · ABSTRACT. In recent years, offline reinforcement learning (RL) algorithms have gained considerable attention. However, the role of data ...
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Jun 27, 2023 · In this study, we propose a memory technique, (Prioritized) Trajectory Replay (TR/PTR), which extends the sampling perspective to trajectories for more ...
Full Research Paper ~ A Trajectory Perspective on the Role of Data Sampling Techniques in Offline Reinforcement Learning (Page 1229). Extended Abstract ...
This section provides an overview of fundamental data sampling methods and offline RL algorithms, which inspire our considerations of trajectory-based ...
However, the role of data sampling techniques in offline RL has been overlooked despite its potential to enhance online RL performance. Recent research suggests ...
In summary, our research emphasizes the significance of trajectory-based data sampling techniques in enhancing the efficiency and performance of offline RL ...
○ Trajectory Data: Each offline sample is a complete trajectory of the form (x1,a1,r1,x2,a2,r2,...,. xH) sampled in the underlying MDP using an offline policy ...
Missing: Perspective Techniques
Abstract. This article introduces the theory of offline reinforcement learning in large state spaces, where good policies are learned from historical data.