Reinforcement Learning for Real-Time Gameplay Technique Discovery
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Solution Overview
Problem
Existing systems fail to efficiently discover and evaluate new gameplay techniques in vast gaming environments, lacking real-time determination of popular and engaging metas, policies, and strategies.
Innovation Solution
A system utilizing a determining agent to gather data from various Internet platforms and a deep reinforcement learning agent to play games, determining optimal in-game actions and strategies through a training and exploration loop, with a reward mechanism based on metrics such as skill, novelty, popularity, humor, and enjoyment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional game analysis methods are used, then system complexity is low, but the ability to discover and evaluate new gameplay techniques in real-time is insufficient
Solution Approach 1:
The patent introduces an intermediary system comprising a determining agent and reinforcement learning agent that mediates between the game environment and the analysis process. This intermediary layer enables automated real-time discovery and evaluation of gameplay techniques by translating game states into learnable representations and evaluating them through trained models, thereby achieving high productivity without requiring direct complex human analysis of every game state
Solution Approach 2:
The patent replaces traditional mechanical analysis methods with machine learning-based automated analysis. The reinforcement learning agent and determining agent substitute manual game analysis with algorithmic processing, using trained neural networks to evaluate game states and discover techniques automatically, thus achieving real-time analysis capability while managing system complexity through software-based solutions
2Measurement precision
If comprehensive data from multiple Internet platforms is collected, then the quality of determined reward frameworks improves, but data processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-training the reinforcement learning agent and determining agent using comprehensive data from multiple Internet platforms before deployment. During actual game analysis, the pre-trained models rapidly evaluate states without requiring real-time processing of raw platform data, thus achieving high measurement precision in reward frameworks while minimizing data processing time during operational use
Solution Approach 2:
The patent creates simplified copies or representations of complex game states and platform data that can be processed efficiently. The determining agent generates condensed state representations that capture essential features needed for reward framework determination, allowing accurate analysis without processing the full complexity of original multi-platform data in real-time
Data Source
AI summary
Exemplary embodiments include reinforcement learning systems and methods for discovering new techniques for playing a game. An exemplary system comprises: A determining agent configured to search at least one Internet platform for data related to a game scenario and determine at least one reward framework based on results from the search of the Internet platform, the reward framework being determined by at least one metric for a characteristic of the game scenario; and a reinforcement learning agent configured to perform a training and exploration loop comprising a plurality of iterations, each iteration comprising: playing at least one game scenario within a game by taking sequential in-game actions available in the game scenario; transmitting results of each of the plurality of sequential in-game actions to the determining agent; and receiving a reward for successful progression through the game scenario according to the reward framework determined by the determining agent.