← Back to all news
arXiv AI By Dongyin Zhuo, Fengjunjie Pan, Nenad Petrovic, Alois Knoll

Porimon: An LLM-Based Pok\'emon Battle Agent Enhanced by Long/Short-Term Knowledge Augmented Generation

Read the original on arXiv AI →

The paper introduces Porimon, an LLM-based agent for Pokémon Battles that uses Long/Short-Term Knowledge Augmented Generation (LSTKAG) to incorporate past states of the current task and retrieve experience summaries from similar previous instances. Porimon also employs an external API for precise damage calculation and detailed game information. Experimental results from 15,000 simulated battles show that Porimon, with hyperparameter optimization, outperforms both the earlier PokéLLMon agent and a rule-based heuristic player, and ablation studies confirm the benefit of the game information retrieval extension, though the impact of Long-Term KAG remains inconclusive.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv AI
Oct 7

SpeedrunBench: Challenging LLM Agents with Video Game Speedrunning

SpeedrunBench is a new benchmark that tests large language model agents on their ability to develop and refine strategies for completing nine different video games as quickly as possible. The benchmark requires agents to repeatedly improve, reflect, and reason over long action horizons to beat both themselves and other players. Experiments show that while agents can approach human records on simple platformers, they lag behind humans on longer, more complex games within realistic resource limits.

By Yoshinari Fujinuma, Keisuke Kamahori, Ryuto Koike, Abdelrahman Madkour, Varun Prashant Gangal, Monty Bichouna, Martyna Markiewicz, Shivani Jain, Duncan Curtis, Rebecca Qian, Anand Kannappan
llmsagentsbenchmarks
More like this →
arXiv AI
Oct 7

Learn2Play Bench: How Well Do LLM Agents Learn from Experience in Unfamiliar Environments?

Learn2Play Bench is a new benchmark that tests how well large language model agents learn from experience in unfamiliar, text‑based games with novel or counterintuitive rules. The benchmark provides reproducible feedback, automatic scoring, and varied game instances to evaluate learning across repeated attempts and transfer to new situations. Findings show that retaining full action records aids learning, human players outperform agents, and the choice of harness significantly impacts performance and inference cost.

By Yibo Li, Jinhang Qiu, Zhi Zheng, Qianyun Guo, Jiaying Wu, Shuo Ji, Bryan Hooi
llmsagentsbenchmarks
More like this →
arXiv Machine Learning
Sep 4

LLM-Guided Reinforcement Learning for Adaptive NPC Behavior in Multi-Agent Combat Games

The paper explores a runtime strategy-selection framework where a large language model (LLM) guides a pre‑trained reinforcement learning (RL) policy for non‑player characters (NPCs) in a Unity combat game without altering the underlying policy. Five NPC agents sharing a PPO policy were compared in a baseline setup and an LLM‑augmented setup, where a locally hosted Mistral 7B model assigns one of four tactical tags every five seconds based on live game state. Across 600 episodes against three scripted opponents, the LLM‑augmented agents more than doubled their win rate against a Balanced opponent, improved performance against an Evasive opponent, but struggled against an Aggressive opponent due to over‑reliance on encirclement; analysis of 2,430 strategy selections revealed limited zero‑shot differentiation with the model favoring Surround in 83.8% of cases.

By Hrithika Deepu Nair, Kayvan Karim
llmsagentsreinforcement-learning
More like this →