Improve Agents without Retraining: Parallel Tree Search with Off-Policy
Correction
Improve Agents without Retraining: Parallel Tree Search with Off-Policy
Correction
Tree Search (TS) is crucial to some of the most influential successes in reinforcement learning. Here, we tackle two major challenges with TS that limit its usability: \textit{distribution shift} and \textit{scalability}. We first discover and analyze a counter-intuitive phenomenon: action selection through TS and a pre-trained value function often leads …