… For these comparisons, empirical analyzes were applied using … Q-learning is an off-policy
approach and it is a quite good … that we study, but these days are not included into the test set …

… the recent research on offline reinforcement learning based on representation … , offline policy
evaluation and model selection. Moreover, the study trends of offline reinforcement learning …

… SAC combines off-policy updates with a stable stochastic AC … sample efficiency over off-policy
and on-policy prior methods, … : off-policy maximum entropy deep reinforcement learning …

… evaluation method is proposed to separate patient samples into two domains according to
their responses to treatments and the state value of the chosen policy. … reinforcement learning …

… Policy Gradient 的算法,其基本概念便是把 Policy Gradient 從On-Policy 改成Off-Policy 型態,其中
,所謂OnPolicy … 中採取的行為和反應進 行更新;而Off-Policy 則是使用另一個代理儘可能的收集…

… data or other empirical data to learn action strategies offline … sampling strategy for offline
reinforcement learning based on TD-… 为此,研究者们提出离策略(Off-Policy) 强化学习方法,比如异步…

… presented study is to show that reinforcement learning based … The most preferred model-free
reinforcement algorithms in … the main aspects of DDPG is that it is an off-policy algorithm. …

… reinforcement learning, reviews the history of computer Go concurrently, analyzes the
algorithms features, and discusses the research … 蒙特卡罗方法同时还可以与离策 略(off-policy)的…

… passive biped robot based on deep reinforcement learning. By analyzing the hybrid dynamics
… learning of quasi-passive dynamic walking by an unstable biped robot based on off-policy …

… proximal policy optimization (PPO) reinforcement learning … is the result of off-policy learning,
TD-learning, and nonlinear function … When the empirical data in the buffer reaches the set …