论文《Inverse Factorized Soft Q-Learning for Cooperative Multi-agent Imitation Learning》来自 NeurIPS 2024。这篇论文研究多智能体环境中的模仿学习问题,提出 Multi-agent Inverse Factorized Q-learning (…
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...
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...
3.1 Guided Cost Learning Algorithm 之前说过,sample-based updates包含了两种不同的分布:第一项通过来自专家的样本近似得到,第二项通过来自soft policy的采样: 这样的话效率会比较低,我们想使用策略:只用一个分布进行采样。这种策略会引入估计中的偏差,因为我们使用的是一个不完美的分布。为了减轻这种偏差,可以使用重...
However, more advanced RL strategies (and newer derivative approaches) such as soft actor-critic106, double deep Q-network107, rainbow deep Q-network108 or proximal policy optimization109 have explored ways to improve in areas such as stability and sample efficiency. We intend to investigate more...
Learning from demonstration, or imitation learning, is the process of learning to act in an environment from examples provided by a teacher. Inverse reinforcement learning (IRL) is a specific form of learning from demonstration that attempts to estimate the reward function of a Markov decision proce...
Learning and inferring underlying motion patterns of captured 2D scenes and then re-creating dynamic evolution consistent with the real-world natural phenomena have high appeal for graphics and animation. To bridge the technical gap between virtual and real environments, we focus on the inverse modelin...
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。
`\pi(a|s) = \exp(Q(s, a) - V(s))`:计算每个状态s下采取动作a的策略概率\pi(a|s),这里使用的是 softmax 函数,将值函数Q(s, a)和V(s)转化为概率分布。 算法3: s 1. 初始化初始状态的访问频率期望: `\mathbb{E}_1[\mu(s_{\text{start}})] = 1`:初始化起始状态s_{\text{start}...
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 and online imitation learning settings, significantly outperforming existing methods both in the number of required environment...