#sample-efficiency2 articles

2 articles

AINeutralarXiv – CS AI · 4h ago0

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Adaptive Correlation-Weighted Intrinsic Rewards for Reinforcement Learning

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.

AINeutralarXiv – CS AI · 4h ago0

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Offline-to-Online Multi-Agent Reinforcement Learning with Offline Value Function Memory and Sequential Exploration

Researchers propose OVMSE, a new framework for Offline-to-Online Multi-Agent Reinforcement Learning that addresses key challenges in transitioning from offline training to online fine-tuning. The framework introduces Offline Value Function Memory and Sequential Exploration strategies to improve sample efficiency and performance in multi-agent environments.