A Comprehensive Survey on Game Playing Agents and Large Models: Methods, Applications, and Challenges

Core Concepts

Large-scale models (LMs) are revolutionizing game playing scenarios by enhancing agent capabilities and generalizability, but challenges like hallucinations, error correction, generalization, and interpretability persist.

Abstract

The paper explores the evolution of Large-scale Models (LMs) in gaming environments, emphasizing their impact on complex tasks. It reviews the current landscape of LM-based Agents (LMAs) for games, highlighting commonalities and challenges. The study delves into perception, inference, action cycles in LMAs with a focus on memory retrieval, learning strategies, reasoning processes, decision-making mechanisms, reflection capabilities. Challenges like hallucinations due to critic agents or structured reasoning errors are addressed. Error correction methods involve iterative prompting with feedback mechanisms or chat chains among models. Generalization techniques include zero-shot learning approaches or role-specific prompt engineering for unseen tasks. Interpretability is enhanced through COT reasoning frameworks or structured communication methods for transparent decision-making.

Stats

Recent advancements in LMs have led to notable achievements across various applications [93], [91], [16].
Investigating the performance of LMAs within complex gaming environments is crucial for delineating their current limitations [42; 43; 44].
Four significant challenges in LMAs include addressing hallucinations in both critic agents and structured reasoning [24; 78], correcting errors through iterative learning or feedback [32], generalizing learnt knowledge to unseen tasks using zero-shot learning or structured adaptability [64; 90], and ensuring interpretability through transparent decision-making processes.
Multi-modal perception enhancements could lead to more sophisticated task handling and immersive game playing experiences.
Authenticity in gaming scenarios can be improved by better grounding LLM generations in the game's narrative and state.
Use of external tools to enhance gameplay represents a significant gap in achieving AGI.
Mastering real-time gaming presents a formidable challenge due to the demanding nature of gaming environments.

Quotes

"LMs exhibit advanced language comprehension skills using implicit knowledge to adapt to new challenges."
"The ability of LMAs to act effectively within dynamic gaming environments is crucial for progress towards autonomy."
"Enhancing multi-modal capabilities could lead to more sophisticated task handling and immersive game playing experiences."

Key Insights Distilled From

A Survey on Game Playing Agents and Large Models

by Xinr... at arxiv.org 03-18-2024

https://arxiv.org/pdf/2403.10249.pdf

Deeper Inquiries

How can LMAs be further improved to handle real-time gaming demands efficiently?

LMAs can be enhanced to meet the real-time demands of gaming by focusing on optimizing their inference speed and decision-making processes. One approach is to streamline the model architecture, reducing unnecessary complexity that may slow down response times. Additionally, implementing specialized hardware or software accelerators tailored for gaming tasks can significantly boost performance. Another strategy involves pre-computing certain actions or decisions based on common scenarios in games, allowing LMAs to respond quickly without extensive computation during gameplay. Furthermore, exploring techniques like reinforcement learning for adaptive real-time decision-making and incorporating predictive models for anticipating game states can also improve efficiency in handling real-time gaming demands.

What are the potential implications of enhancing multi-modal perception capabilities in LMAs for future gaming experiences?

Enhancing multi-modal perception capabilities in LMAs has profound implications for future gaming experiences. By integrating visual, auditory, and textual information seamlessly, LMAs can provide a more immersive and interactive gameplay environment. This enhancement enables agents to understand context better, interpret complex scenarios accurately, and engage with players more effectively through natural language interactions. Moreover, improved multi-modal perception allows LMAs to adapt dynamically to changing game environments and player inputs, leading to personalized and engaging gameplay experiences tailored to individual preferences. Overall, enhancing multi-modal perception capabilities opens up new possibilities for creating realistic virtual worlds where players can interact with intelligent agents in a more intuitive manner.

How can external tools be effectively integrated into LMAs to enhance gameplay experiences beyond traditional AI approaches?

Integrating external tools into LMAs requires a strategic approach focused on seamless interaction between the agent and external resources. One way is through API integration that allows direct communication between the LMA and external databases or services relevant to the game environment. By leveraging APIs effectively, LMAs gain access to valuable information such as game guides or walkthroughs that enhance decision-making processes during gameplay.
Another method involves utilizing pre-trained models specifically designed for interacting with external tools like documentation or tutorials related to the game world. These models enable efficient extraction of relevant data from external sources while maintaining coherence within the LMA's decision-making framework.
Furthermore, incorporating reinforcement learning techniques that reward successful utilization of external tools encourages adaptive behavior in responding intelligently based on information obtained from these resources.
Overall, effective integration of external tools empowers LMAs with additional knowledge resources beyond their internal training data sets, enriching their understanding of complex game environments and enhancing overall gameplay experiences beyond traditional AI approaches.