Dataset from ES-C51: Expected Sarsa Based C51 Distributional Reinforcement Learning Algorithm

This dataset contains the results of experiments comparing the performance of the standard Q-learning based distributional deep reinforcement learning algorithm QL-C51, and a novel variant which uses Expected-Sarsa temporal difference updates (ES-C51). Each algorithm was executed for 10 separate runs with independent seeds on 22 environments (Acrobot, Cartpole, and the Atari-10 environments with and without stochasticity). Results are reported for each run in terms of the mean episodic reward over the last 10% of learning episodes. Full details are in the corresponding paper.

该数据集包含实验结果，用于比较标准的基于Q学习（Q-learning）的分布型深度强化学习算法QL-C51，与采用期望Sarsa（Expected-Sarsa）时间差分更新的新型变体算法ES-C51的性能。每种算法在22个环境（Acrobot、Cartpole，以及带随机性和无随机性的Atari-10环境）上使用独立种子执行10次独立运行。结果报告了每次运行中学习过程最后10%回合的平均回合奖励。完整细节参见相应论文。