On standard benchmarks, the implicitly learned rewards show a high positive correlation with the ground-truth rewards, illustrating our method can also be used for inverse reinforcement learning (IRL). Our method, Inverse soft-Q learning (IQ-Learn) obtains state-of-the-art results in offline ...
Inverse Q-Learning (IQ-Learn) is a a simple, stable & data-efficient framework for Imitation Learning (IL), that directly learns soft Q-functions from expert data. IQ-Learn enables non-adverserial imitation learning, working on both offline and online IL settings. It is performant even with...
几篇论文实现代码:《IQ-Learn: Inverse soft-Q Learning for Imitation》(NeurIPS 2021) GitHub: github.com/Div99/IQ-Learn [fig2] 《NAS-Bench-x11 and the Power of Learning Curves》(NeurIPS 2021) GitHub...
We introduce Inverse Q-Learning (IQ-Learn), a state-of-the-art novel framework for Imitation Learning (IL), that directly learns soft Q-functions from expert data. IQ-Learn enables non-adverserial imitation learning, working on both offline and online IL settings. It is performant even with...
In a single-agent setting, IL has proven to be done efficiently through an inverse soft-Q learning process given expert demonstrations. However, extending this framework to a multi-agent context introduces the need to simultaneously learn both local value functions to capture local observations and ...
Imitation Learning (IL) is a powerful approach for constructing human-like NPCs in games. Unlike general games, metaverse games tend to build up the more complex, and more diverse game characters. Data between distinct roles is not interoperable, which l
其中Q_{soft}代表 soft Q function,为: 这里作者引用了Reinforcement learning with deep energy-based policies (2017) 以及Modeling purposeful adaptive behavior with the principle of maximum causal entropy(2010) , 后续将跟进这两篇文章,以证明上式的结论。
类似softmax,reward相等就概率相同,reward之比越大,概率会指数级区分开。分母上是partition function Z(θ) ,在有限步数时能收敛,在无限步数时需要对reward加discount factor才能收敛。 2.2,非确定性MDP的路径分布(agent获得评估轨迹的能力) 其中indicator function I_{\zeta\in o} 一般=1,所以此处忽略了。eq.4...
and then the system transitions to the next state. The agent uses the collected reward to update its expectation of future reward, e.g. theQ-value. There are several algorithms for updating theQ-values including Monte Carlo learning, SARSA, andQ-learning. For more information on RL, see (...
3 Using machine learning, new platform can program the transformation of 2D stretchable surfaces into specific 3D shapes. January 27th, 2022 - By:Technical Paper Link Abstract “Across fields of science, researchers have increasingly focused on designing soft devices that can shape-morph to achieve ...