#sparse-rewards2 articles

2 articles

AIBullisharXiv – CS AI · 6h ago6

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Actor-Critic for Continuous Action Chunks: A Reinforcement Learning Framework for Long-Horizon Robotic Manipulation with Sparse Reward

Researchers introduced AC3 (Actor-Critic for Continuous Chunks), a new reinforcement learning framework that addresses challenges in long-horizon robotic manipulation tasks with sparse rewards. The system uses continuous action chunks with stabilization mechanisms and achieved superior performance on 25 benchmark tasks using minimal demonstrations.

AINeutralarXiv – CS AI · 6h ago1

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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.