2 papers
cs.LG2025
Learning to Explore in Diverse Reward Settings via Temporal-Difference-Error Maximization
Sebastian Griesbach, Carlo D'Eramo
Numerous heuristics and advanced approaches have been proposed for exploration in different settings for deep reinforcement learning. Noise-based exploration generally fares well w…
cs.LG2024
Deterministic Exploration via Stationary Bellman Error Maximization
Sebastian Griesbach, Carlo D'Eramo
Exploration is a crucial and distinctive aspect of reinforcement learning (RL) that remains a fundamental open problem. Several methods have been proposed to tackle this challenge.…