Geometrically Averaged Hard Target Updates for Linear Q-Learning
📰 ArXiv cs.AI
Learn to stabilize linear Q-learning using geometrically averaged hard target updates for improved performance
Action Steps
- Implement periodic hard target updates in linear Q-learning
- Apply geometric averaging to target updates using lambda-geometric weights
- Tune the lambda parameter to optimize stability and performance
- Compare the results of geometrically averaged hard target updates with traditional target updates
- Analyze the effect of lambda on the convergence of Q-learning
Who Needs to Know This
Researchers and engineers working on reinforcement learning and Q-learning algorithms can benefit from this technique to improve stability and performance in their models. This can be particularly useful in applications where linear function approximation is used.
Key Insight
💡 Geometric averaging of hard target updates can lead to improved stability and performance in linear Q-learning
Share This
💡 Geometrically averaged hard target updates can improve stability in linear Q-learning! #Qlearning #ReinforcementLearning
Key Takeaways
Learn to stabilize linear Q-learning using geometrically averaged hard target updates for improved performance
Full Article
Title: Geometrically Averaged Hard Target Updates for Linear Q-Learning
Abstract:
arXiv:2606.10835v1 Announce Type: cross Abstract: Periodic hard target updates are among the most common stabilization devices in modern deep Q-learning. Recent studies suggest that target updates can improve stability in Q-learning with function approximation, including linear function approximation. We introduce and analyze the so-called $\lambda$-target update, obtained by averaging the $m$-periodic target update maps with $\lambda$-geometric weights $(1-\lambda)\lambda^{m-1}$, $\lambda \in [
Abstract:
arXiv:2606.10835v1 Announce Type: cross Abstract: Periodic hard target updates are among the most common stabilization devices in modern deep Q-learning. Recent studies suggest that target updates can improve stability in Q-learning with function approximation, including linear function approximation. We introduce and analyze the so-called $\lambda$-target update, obtained by averaging the $m$-periodic target update maps with $\lambda$-geometric weights $(1-\lambda)\lambda^{m-1}$, $\lambda \in [
DeepCamp AI