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This paper presents a novel approach to enhance sample efficiency in reinforcement learning (RL) by incorporating prior knowledge into actor-critic algorithms. The authors propose a systematic framework that utilizes pseudo-labels derived from existing solutions to improve exploration and learning speed across various RL tasks. Experimental results demonstrate significant improvements in performance and robustness, even when using imprecise prior knowledge, while maintaining low computational overhead.
- Author
- sergiosvieira
- Language
- EN