Adversarial RL Agents for Game Environment Generalization
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Solution Overview
Problem
Reinforcement learning (RL) agents trained for computer game environments tend to overfit due to limited diverse training datasets, making them less generalizable and adaptable to changing environments, which hampers their usefulness in real-time game development and gameplay.
Innovation Solution
Adversarial deep reinforcement-learning techniques are employed to train a Generator RL agent for procedural content generation and a Solver RL agent to interact with the generated environments, using iterative reward signals to encourage diversity and challenge, while freezing agent states for efficient training and updating.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If RL agents are trained on specific computer game environments to achieve super-human performance, then solving performance is improved, but generalizability to unseen environments deteriorates due to overfitting
Solution Approach 1:
The patent applies dynamics by making the training environment dynamic and adaptive through the Generator RL agent that continuously creates new, diverse game environments. The training process transitions from static pre-defined environments to dynamically generated ones, allowing the Solver agent to adapt to varying conditions and improve generalizability while maintaining solving performance
Solution Approach 2:
The patent changes the parameters of the training process by introducing adversarial reinforcement learning where the Generator and Solver agents have opposing objectives. The Generator maximizes environment diversity while the Solver maximizes solving performance, creating a parameter optimization framework that balances overfitting prevention with performance maintenance through reward signal manipulation
2Adaptability or versatility
If designers create diverse training datasets using scripting to improve generalizability, then adaptability is improved, but the quantity and diversity of training environments remain limited due to manual constraints
Solution Approach 1:
The patent applies self-service by enabling the system to generate its own training environments autonomously through the Generator RL agent. Instead of relying on manual scripting by designers, the Generator automatically creates diverse game environments with varying parameters, obstacles, and configurations, significantly increasing the quantity and diversity of training data without human intervention
Solution Approach 2:
The patent substitutes the mechanical system of manual environment scripting with an automated intelligent system. The Generator RL agent replaces human designers in creating training environments, using reinforcement learning to autonomously generate diverse scenarios, thereby overcoming the limitations of manual scripting in terms of quantity, diversity, and consistency
3Reliability
If RL agents are trained on fixed training sets to achieve consistent performance, then reliability is improved, but adaptability to changing game environments deteriorates
Solution Approach 1:
The patent resolves this contradiction by making the training environments dynamic rather than fixed. The Generator continuously creates new environments with varying parameters, ensuring the Solver is trained on diverse scenarios. This dynamic approach maintains reliability through consistent training rigor while improving adaptability to changing game environments
Solution Approach 2:
The patent implements feedback mechanisms where the Solver agent's performance on generated environments provides reinforcement signals to the Generator. This feedback loop ensures that environments are consistently challenging yet solvable, maintaining performance reliability while the varying environment parameters ensure adaptability to changes
Data Source
AI summary
Methods, apparatus and systems are provided for training a first reinforcement-learning (RL) agent and a second RL agent coupled to a computer game environment using RL techniques. The first RL agent iteratively generates a sub-goal sequence in relation to an overall goal within the computer game environment, where the first RL agent generates a new sub-goal for the sub-goal sequence after a second RL agent, interacting with the computer game environment, successfully achieves a current sub-goal in the sub-goal sequence. The second RL agent iteratively interacts with the computer game environment to achieve the current sub-goal in which each iterative interaction includes an attempt by the second RL agent for interacting with the computer game environment to achieve the current sub-goal. The first RL agent is updated using a first reward issued when the second RL agent successfully achieves the current sub-goal. The second RL agent is updated when a second reward is issued by the computer game environment based on the performance of the second RL agent attempting to achieve said current sub-goal. Once validly trained, the first RL agent forms a final first RL agent for automatic procedural content generation (PCG) in the computer game environment and the second RL agent forms a final second RL agent for automatically interacting with a PCG computer game environment.