Algorithms for inverse reinforcement learning该论文是吴恩达老师2000年的工作,也是入门逆强化学习(Inverse Reinforcement Learning, IRL)的基础。以下是我对该文章的理解和总结,欢迎大家一起学习并批评和指…
模仿学习。reward function在强化学习里面非常非常重要,是对行为的抽象精简的描述,因此IRL (Inverse Reinforcement Learning)可能是一种很高效的模仿学习范式。 III) 一些强化学习相关名词的定义: (包括:MDP,policy,value function,q-function,optimal value function, optimal q-function,Bellman equations, Bellman Optimal...
《Algorithms for Inverse Reinforcement Learning》论文核心内容概述：核心任务：该论文的核心任务是探讨如何通过观察智能体的行为，推断出隐藏的奖励函数。这是逆强化学习的基础任务之一，旨在逆向工程出驱动智能体行为的潜在规则。有限状态空间场景：在有限状态空间的场景下，论文假设最优策略已知。它详细阐述了...
1. MDPs 在之前一篇博文中讲过了 Q函数 2.IRL in Finite State Spaces 归为优化 这个优化的形式,使最小中的最大,不由让人想到SVM(事实的确有这样一篇文章) 3. Linear Function Approximation in Large State Spaces R(s) =\Sum_{i=1}^{d} \alpha_i \phi_i(s) 4. IRL from Sampled Trajectories ...
深入探索逆强化学习领域的基石之作，吴恩达教授在2000年的经典论文《Algorithms for Inverse Reinforcement Learning》为我们揭示了这一领域的入门奥秘。本文将简要概述论文的核心内容，旨在帮助读者理解并进一步探讨。首先，对于有限状态空间的场景，论文假设了最优策略已知，它探讨了如何通过观察智能体的行为，...
For example, "score a goal" or "cross the grey line". In the example above, the recovered reward function explained the observed behaviour and yet wasn't interpretable in the same way as the true reward function. Future Work Add Ziebart's Maximum Entropy Inverse Reinforcement Learning and ...
Contains JAX implementation of algorithms forinverse reinforcement learning(IRL). Inverse RL is an online approach to imitation learning where we try toextract a reward functionthat makes the expert optimal. IRLdoesn't suffer from compounding errors(like behavioural cloning) and doesn't need expert ...
Framework and Algorithms for Online Inverse Reinforcement Learning Under Imperfect ObservationsAutonomous systems predominantly deploy IRL (inverse reinforcement learning) to model the task preferences of a user (often called an expert), as a reward function, by observing the user while executing the ...
Chen, Y., Liu, J., Khoussainov, B.: Maximum entropy inverse reinforcement learning for mean field games. http://arxiv.org/abs/2104.14654 (2021) Cui, K., Koeppl, H.: Approximately solving mean field games via entropy-regularized deep reinforcement learning. In: International Conference on ...
Special Issue on Ensemble Learning and/or Explainability Pintelas, PanagiotisLivieris, Ioannis E. E. Solving of the Inverse Boundary Value Problem for the Heat Conduction Equation in Two Intervals of Time AlNuaimi, Bashar TalibAlMahdawi, H. K.Albadran, ZainalabideenAlkattan, HusseinAbotaleb, ...