Researchers at UC San Diego Propose DrS: A Novel Machine Learning Approach for Learning Reusable Dense Rewards for Multi-Stage Tasks in a Data-Driven Manner

The success of many reinforcement learning (RL) techniques relies on dense reward functions, but designing them can be difficult due to expertise requirements and trial and error. Sparse rewards, like binary task completion signals, are easier to obtain but pose challenges for RL algorithms, such as exploration. Consequently, the question emerges: Can dense reward functions […]

The post Researchers at UC San Diego Propose DrS: A Novel Machine Learning Approach for Learning Reusable Dense Rewards for Multi-Stage Tasks in a …

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