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🧠 AI⚪ NeutralImportance 4/10
Adaptive Correlation-Weighted Intrinsic Rewards for Reinforcement Learning
🤖AI Summary
Researchers propose ACWI, a new reinforcement learning framework that dynamically balances intrinsic and extrinsic rewards through adaptive scaling coefficients. The system uses a lightweight Beta Network to optimize exploration in sparse reward environments, demonstrating improved sample efficiency and stability in MiniGrid experiments.
Key Takeaways
- →ACWI introduces adaptive intrinsic reward scaling that learns state-dependent coefficients online rather than using fixed manual tuning.
- →The framework employs a Beta Network with encoder-based architecture to predict optimal intrinsic reward weights from agent states.
- →A correlation-based objective aligns weighted intrinsic rewards with discounted future extrinsic returns for better exploration.
- →Experimental results show consistent improvements in sample efficiency and learning stability with minimal computational overhead.
- →The approach addresses key limitations of conventional reinforcement learning methods in sparse reward environments.
#reinforcement-learning#adaptive-scaling#intrinsic-rewards#exploration#sparse-rewards#beta-network#sample-efficiency#machine-learning
Read Original →via arXiv – CS AI
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