Efficient Sample Reuse in Policy Gradients with Parameter-Based Exploration

DOI10.1162/NECO_a_00452zbMath1414.68090arXiv1301.3966OpenAlexW2133224499WikidataQ47904761 ScholiaQ47904761MaRDI QIDQ5378202

Voot Tangkaratt, Tingting Zhao, Masashi Sugiyama, Jun Morimoto, Hirotaka Hachiya

Publication date: 12 June 2019

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1301.3966

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Artificial intelligence for robotics (68T40)

Related Items

Model-based policy gradients with parameter-based exploration by least-squares conditional density estimation, Model-based reinforcement learning with dimension reduction

Cites Work

• Analysis and improvement of policy gradient estimation
• Approximate dynamic programming with a fuzzy parameterization
• Improving predictive inference under covariate shift by weighting the log-likelihood function
• Real-time reinforcement learning by sequential actor-critics and experience replay
• Simple statistical gradient-following algorithms for connectionist reinforcement learning
• Reward-Weighted Regression with Sample Reuse for Direct Policy Search in Reinforcement Learning
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item