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)

Python Programming Learning Circle

Feb 20, 2025 · Artificial Intelligence

Building a StarCraft II AI Bot with DeepMind's pysc2 in Python

This article provides a step‑by‑step guide, complete with Python code examples, for creating a Protoss AI bot using DeepMind's pysc2 library to mine resources, construct buildings, train units, implement scouting, and execute attack strategies against increasingly difficult computer opponents in StarCraft II.

AI botDeepMindReinforcement Learning

0 likes · 28 min read

Building a StarCraft II AI Bot with DeepMind's pysc2 in Python

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

Building a StarCraft II AI Bot with DeepMind's pysc2 in Python

Building a StarCraft II AI Bot with DeepMind's pysc2 in Python