PPO比其他算法更robust(稳健),这与她使用了 Minorize-Maximization (MM algorithm)有很大关联,这保证了PPO每次更新策略 总能让性能获得单调的提升,详见RL — Proximal Policy Optimization (PPO) Explained - 2018-07 - Jonathan Hui这是介绍PPO算法极好的文章,写在版权保护意识很强的 Medium网站,大陆不能正常访问。
The true online TD({\\lambda}) algorithm has recently been proposed (van Seijen and Sutton, 2014) as a universal replacement for the popular TD({\\lambda}) algorithm, in temporal-difference learning and reinforcement learning. True online TD({\\lambda}) has better theoretical properties than ...
This app implements the TD(0) algorithm, described in Sutton's classic bookReinforcement Learning: An Introduction, in Swift. There're 6046 unique states in total and the code trains by self-play using the TD(0) to update the state values for the states. In the first run of the app, ...
In other words, we're keeping a (decaying) trace of where the agent has been previously (the decay strength is controlled by a hyperparameter \\(\lambda\\)), and performing Q value updates not only on one link of the s,a,r,s,a,s,a,r... chain, but along some recent history of...
TD( \\(\\lambda \\) ) has become a crucial algorithm of modern reinforcement learning (RL). By introducing the trace decay parameter \\(\\lambda \\) , TD( \\(\\lambda \\) ) elegantly unifies Monte Carlo methods ( \\(\\lambda =1\\) ) and one-step temporal difference prediction...