Distributional Reinforcement Learning with Ensembles
It is well known that ensemble methods often provide enhanced performance in reinforcement learning. In this paper, we explore this concept further by using group-aided training within the distributional reinforcement learning paradigm. Specifically, we propose an extension to categorical reinforcement learning, where distributional learning targets are implicitly based on the total information gathered by an ensemble. We empirically show that this may lead to much more robust initial learning, a stronger individual performance level, and good efficiency on a per-sample basis.
Code (0)
등록된 구현이 없습니다.
Tasks
Distributional Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Similar Papers 제목 키워드 기반
Diverse Projection Ensembles for Distributional Reinforcement Learning
In contrast to classical reinforcement learning (RL), distributional RL algorithms aim to learn the distribution of returns rather than their expected value. Since the nature of the return distribution is generally unkno…
Distributional Reinforcement LearningDiversityInductive Biasreinforcement-learning+2FlowCritic: Bridging Value Estimation with Flow Matching in Reinforcement Learning
Reliable value estimation serves as the cornerstone of reinforcement learning (RL) by evaluating long-term returns and guiding policy improvement, significantly influencing the convergence speed and final performance. Ex…
Reinforcement LearningValue predictionAggressive Q-Learning with Ensembles: Achieving Both High Sample Efficiency and High Asymptotic Performance
Recent advances in model-free deep reinforcement learning (DRL) show that simple model-free methods can be highly effective in challenging high-dimensional continuous control tasks. In particular, Truncated Quantile Crit…
continuous-controlContinuous ControlDeep Reinforcement LearningMuJoCo+2Beyond Discriminant Patterns: On the Robustness of Decision Rule Ensembles
Local decision rules are commonly understood to be more explainable, due to the local nature of the patterns involved. With numerical optimization methods such as gradient boosting, ensembles of local decision rules can …
Developing parsimonious ensembles using predictor diversity within a reinforcement learning framework
Heterogeneous ensembles that can aggregate an unrestricted number and variety of base predictors can effectively address challenging prediction problems. In particular, accurate ensembles that are also parsimonious, i.e.…
Diversityreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1