Experimental findings indicate that reinforcement learning agents achieve superior consistency and long-term optimization in structured settings, while human players demonstrate greater flexibility and adaptability under uncertain or novel conditions.
A runtime strategy-selection framework in which a large language model (LLM) guides a trained RL policy without modifying its underlying behavior is investigated, demonstrating both the potential and limitations of LLM-guided runtime strategy selection for adaptive multi-agent game AI.
Multi-agent Reinforcement learning has gained significant attention for solving decision-making problems involving multiple autonomous agents. However, effective learning in MARL is still difficult due to environments, dependencies between agents, and poor exploration strategies. Although adaptive exploration and curri...
B. Adwaith, Kevin Francis, Remya Nair T· International Conference on...· 0 citations
The review indicates that ReinforcementLearning has evolved from classical Q-Learning algorithms into Deep Reinforcement Learningcapable of solving high-dimensional decision problems using deep neural networks.
It is argued that chess provides a useful bridge between cognitive and computational perspectives on strategic reasoning, and identifies explicit planning, grounded and faithful explanation, metacognitive calibration, and human--AI complementarity as directions for future research.
This work comprehensively investigates the concept of constant-memory strategies in stochastic games, and uncovers the connection between decision models in single-agent and multi-agent contexts.
Feng-Ming Zhu, Fang-Zhen Lin· Proceedings of the Thirty-Fi...· 0 citations
The results show that adaptive, learned NPCs are possible on commodity hardware and the pipeline described here provides a reproducible template for future games, simulations and educational tools.
Imran Maqsood, Namos Khan, Hijab Noor ul Amin et al.· International Journal of Inn...· 0 citations
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