Demystifying MaxEnt Inverse Reinforcement Learning: Theory, Algorithms, and Practical Implementation

This article provides a comprehensive, step‑by‑step exploration of MaxEnt Inverse Reinforcement Learning, covering its statistical foundations, feature‑expectation matching, algorithmic details, deep extensions, and practical engineering considerations for complex decision‑making tasks.

Deep IRLFeature Matchingimitation learning

0 likes · 21 min read

Demystifying MaxEnt Inverse Reinforcement Learning: Theory, Algorithms, and Practical Implementation

Meituan Technology Team

Feb 20, 2025 · Artificial Intelligence

Offline Multi-Agent Reinforcement Learning via In‑Sample Sequential Policy Optimization (InSPO)

The paper introduces InSPO, an offline multi‑agent reinforcement‑learning algorithm that integrates behavior‑regularized Markov games with in‑sample sequential policy updates, using inverse KL divergence and maximum‑entropy regularization to avoid out‑of‑distribution joint actions, improve coordination, and achieve monotonic improvement toward Quantized Response Equilibrium, validated on XOR, bridge, and StarCraft II benchmarks.

StarCraft IIbehavior regularizationbridge game

0 likes · 19 min read

Offline Multi-Agent Reinforcement Learning via In‑Sample Sequential Policy Optimization (InSPO)

Hulu Beijing

Mar 1, 2018 · Artificial Intelligence

Understanding Probabilistic Graphical Models: Bayesian & Markov Networks Explained

This article introduces probabilistic graphical models, explains the differences between Bayesian and Markov networks, derives their joint probability distributions, and details the principles and graphical representations of naive Bayes and maximum entropy models with illustrative equations and diagrams.

Bayesian networkNaive Bayesmarkov network

0 likes · 10 min read

Understanding Probabilistic Graphical Models: Bayesian & Markov Networks Explained