Maximum Entropy — 3 Technical Articles

Aug 9, 2025 · Artificial Intelligence

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)

Offline multi‑agent reinforcement learning (MARL) faces challenges such as out‑of‑distribution joint actions and local optima, and this article introduces the In‑Sample Sequential Policy Optimization (InSPO) algorithm—leveraging inverse KL regularization, maximum‑entropy, and cooperative Markov games—to achieve monotonic policy improvement and superior performance across benchmark tasks.

InSPOMaximum Entropycooperative Markov game

0 likes · 18 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.

Maximum EntropyNaive Bayesbayesian network

0 likes · 10 min read

Understanding Probabilistic Graphical Models: Bayesian & Markov Networks Explained