Memory Merge DQN Stabilizes Reinforcement Learning with Weighted Target Updates.
Summary
Memory Merge DQN introduces a new target network update mechanism for Deep Q-networks that merges recent historical online network copies based on Q-value sensitivity. This approach stabilizes bootstrapped value learning, preserves useful value function structure, and outperforms standard DQN and other baselines on Atari environments.
Why it matters
AI researchers and engineers developing reinforcement learning agents can achieve more stable and higher-performing models by adopting this novel target network update mechanism, leading to more robust AI behaviors in complex environments.
How to implement this in your domain
- 1Investigate the concept of Q-value sensitivity and its application in weighting network parameters.
- 2Experiment with implementing a short memory of historical network copies for target network construction.
- 3Integrate the Memory Merge update mechanism into existing DQN-based reinforcement learning projects.
- 4Benchmark the stability and performance gains against standard DQN and other baselines on relevant tasks.
Who benefits
Key takeaways
- Memory Merge DQN improves stability in Deep Q-networks.
- It merges historical network copies based on Q-value sensitivity.
- This preserves useful value function structure during long-horizon learning.
- The method outperforms standard DQN and other baselines on Atari environments.
Original post by Adrian Ly, Richard Dazeley, Peter Vamplew, Sunil Aryal, Francisco Cruz
"arXiv:2607.19397v1 Announce Type: new Abstract: Deep Q-networks use target networks to stabilise bootstrapped value learning, but the standard hard copy update also introduces a tradeoff. Holding the target network fixed, improves short term stability, yet each hard update abrupt…"
View on XOriginally posted by Adrian Ly, Richard Dazeley, Peter Vamplew, Sunil Aryal, Francisco Cruz on X · view source
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