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Found 4 papers, 3 papers with code
Learning to Modulate pre-trained Models in RL
That is, the performance on the pre-training tasks deteriorates when fine-tuning on new tasks.
Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement Learning
Therefore, exploration strategies and learning methods are required that are capable of tracking the steady domain shifts, and adapting to them.
Fast and Data-Efficient Training of Rainbow: an Experimental Study on Atari
This paper's contribution is threefold: We (1) propose an improved version of Rainbow, seeking to drastically reduce Rainbow's data, training time, and compute requirements while maintaining its competitive performance; (2) we empirically demonstrate the effectiveness of our approach through experiments on the Arcade Learning Environment, and (3) we conduct a number of ablation studies to investigate the effect of the individual proposed modifications.
Towards a General Framework for ML-based Self-tuning Databases
Machine learning (ML) methods have recently emerged as an effective way to perform automated parameter tuning of databases.
