Cooperative Game Agent Creation Using Imitation Learning and Clustering
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Solution Overview
Problem
Existing video game technologies lack the ability for agents to effectively collaborate with users in multiplayer games, particularly in scenarios requiring multiple users, due to limitations in existing learning technologies and the inability for users and agents to communicate at the same level.
Innovation Solution
A method of creating cooperative agents through imitation learning, clustering game action data, and forming an agent pool to facilitate collaboration with users, using techniques like behavior cloning, apprentice learning, and inverse reinforcement learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing learning technology is used for agents in multiplayer games, then agents can perform individual tasks, but agents cannot effectively collaborate with users or other agents at the same communication level
Solution Approach 1:
The patent creates virtual users by copying and cloning actual user gameplay behaviors through behavior cloning technology. The virtual users are trained to replicate human player actions, communication patterns, and decision-making processes, enabling them to interact with other players in a manner indistinguishable from real users. This allows agents to collaborate effectively in multiplayer environments without sacrificing communication effectiveness.
Solution Approach 2:
The patent transforms the agent's capability parameters by integrating multiple learning approaches (behavior cloning, apprentice learning, inverse reinforcement learning) to enhance collaboration skills. The system adjusts the agent's behavioral parameters to match human communication patterns, enabling effective interaction while maintaining reliability through diverse training methodologies.
2Extent of automation
If all users are replaced with agents to perform collaborative tasks, then agent learning can be applied, but the ability to replace users dynamically based on availability is lost
Solution Approach 1:
The patent implements a dynamic system where the composition of players and virtual users can change based on availability. The game server dynamically determines whether to invite real users or virtual users for each game session, allowing flexible adjustment of automation levels. This enables the system to maintain high automation capability while adapting to varying user availability and preferences.
Solution Approach 2:
The patent creates a universal player replacement system where virtual users can substitute for real users in various game scenarios. The behavior cloning models are designed to be universally applicable across different game types and situations, enabling the system to replace users dynamically while maintaining adaptability to different gameplay contexts and compositions.
3Measurement precision
If behavior cloning is used to create virtual users, then user behavior can be accurately replicated, but the system complexity increases due to data collection and training requirements
Solution Approach 1:
The patent performs preliminary data collection and model training during idle periods or between game sessions. Behavior cloning models are trained in advance using collected user gameplay data, so that when games need to be formed, pre-trained virtual users are ready for immediate deployment. This reduces the complexity of real-time behavior replication while maintaining high accuracy.
Solution Approach 2:
The system automatically collects user behavior data and performs self-training of behavior cloning models without requiring manual intervention. The virtual users continuously learn and improve their behavior replication through automated data collection from actual gameplay, reducing the operational complexity while maintaining high measurement precision in behavior replication.
4Adaptability or versatility
If multiple learning approaches (BC, AL, IRL) are integrated, then collaboration capability is enhanced, but the training time and computational resources increase
Solution Approach 1:
The patent segments the learning process into distinct phases, each utilizing different learning approaches for specific purposes. Behavior cloning is used for basic behavior replication, apprentice learning for skill acquisition, and inverse reinforcement learning for strategic understanding. This segmentation allows parallel processing of different learning tasks, reducing overall training time while maintaining enhanced collaboration capability.
Solution Approach 2:
The patent implements a progressive training approach where virtual users receive partial training in different learning approaches based on game requirements. Not all virtual users undergo complete training in all three learning methods; instead, training is tailored to the specific collaboration needs of each game scenario, reducing unnecessary computational overhead and training time while maintaining adequate collaboration capability.
Data Source
AI summary
Provided is a method of automatically creating an agent that may interact with a user in a video game and providing a pre-created agent upon request of the user playing the video game when there is no user to play the video game.