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AI Research
arXiv CS.AI·Jun 24·5 min read
EMAgnet: Parameter-Space EMA Regularization for Policy Gradient Self-Play in Large Games
TL;DR
In large-scale games, traditional policy gradient methods struggle with stability and convergence during self-play. EMAgnet introduces a parameter-space Exponential Moving Average (EMA) regularization technique to enhance these methods.
✦ Why It Matters
Engineers can implement EMAgnet to improve the stability and performance of reinforcement learning agents in large games.
Key Takeaways
- Explore EMAgnet for enhancing agent training stability.
- Integrate EMA regularization in your reinforcement learning models.
- Evaluate performance improvements in large-scale game environments.
- Consider EMAgnet for complex strategic decision-making tasks.
Full Summary
Large games often face challenges in training agents due to instability in policy gradient methods, which can lead to poor performance during self-play. EMAgnet is a novel technique that applies Exponential Moving Average (EMA) regularization directly in the parameter space of policy gradients.
By smoothing the updates to the policy parameters, EMAgnet helps maintain stability and encourages convergence during training. The methodology involves integrating EMA into the training loop of reinforcement learning agents, allowing for more consistent learning signals.
Experimental results demonstrate that agents trained with EMAgnet outperform those using standard policy gradient methods, achieving higher win rates and improved strategic play. These findings suggest that EMAgnet can be a valuable tool for researchers and engineers working on complex game environments, enhancing the robustness of training processes.