AI agents learn complex games via synthetic experience and novel training methods · 7 sources tracked
ByPulseAugur Editorial·[8 sources]·
Researchers are exploring advanced techniques for AI agents to learn complex games, moving beyond simple demonstrations. One approach, GameSmith, uses large language models to synthesize new games for training, improving performance on benchmarks. Another method, VQEL, employs vector quantization for discrete communication protocols in emergent language games, enhancing training stability. Additionally, Player-1 utilizes synthetic experience to train agents for game control, outperforming baseline methods on various environments. These studies highlight the growing sophistication in AI's ability to master intricate tasks through novel training paradigms.
AI
IMPACT
These advancements in game-playing AI could lead to more sophisticated agents capable of complex reasoning and problem-solving in diverse environments.
RANK_REASON
Multiple arXiv papers detailing new research methodologies and models for AI in games.
arXiv:2610.19868v1 Announce Type: cross Abstract: Training LLM agents on long-horizon interactive tasks with reinforcement learning typically relies on human-annotated tasks and reliable verification, both expensive to construct for stateful environments. Self-play offers an alte…
arXiv:2610.17391v1 Announce Type: new Abstract: Amazing breakthroughs in reinforcement learning have taken place. Computers teach themselves to play Chess and Go and beat world champions. There is talk about expanding application areas towards general artificial intelligence (AI)…
arXiv cs.AI
TIER_1English(EN)·Weixian Waylon Li, Jiaxin Zhang, Han Zhou, Chengzu Li, Ke Wang, Fengxiang He, Yiwen Guo·
arXiv:2610.13995v1 Announce Type: cross Abstract: Post-training large language models (LLMs) on two-player zero-sum games provides a verifiable reward, and the gains have been shown to transfer to general reasoning. Existing pipelines, however, rely on a handful of hand-written g…
arXiv:2411.06403v4 Announce Type: replace Abstract: Why can neural networks struggle to learn optimal play in a game with a simple, efficient solution algorithm? Motivated by empirical difficulties with parity in impartial games, we use NIM to study a representational bottleneck …
arXiv:2503.04940v3 Announce Type: replace-cross Abstract: Emergent Language (EL) focuses on the emergence of communication among artificial agents. Although symbolic communication channels more closely mirror the discrete nature of human language, learning such protocols remains …
Computer-use agents have demonstrated strong capabilities in interpreting screens and executing keyboard and mouse actions. However, games, with their visually rich worlds and diverse mechanics, remain challenging and offer a demanding next step toward general digital control. Cu…
arXiv:2610.20584v1 Announce Type: new Abstract: Computer-use agents have demonstrated strong capabilities in interpreting screens and executing keyboard and mouse actions. However, games, with their visually rich worlds and diverse mechanics, remain challenging and offer a demand…
arXiv:2610.18899v1 Announce Type: cross Abstract: Thompson sampling (TS) achieves logarithmic and asymptotically optimal regret in classical stochastic bandits, but its behavior in multi-agent learning remains less understood. We study TS in an unknown finite two-player zero-sum …
