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Methods for Mitigating Sparse Rewards
Dealing with sparse rewards, where feedback is observed only at the end of sequences, is a significant challenge in reinforcement learning. Several general methods have been developed to mitigate the impact of sparse rewards. One common approach is reward shaping, which modifies the original function to provide intermediate feedback. Another method is curriculum learning, which sequentially structures tasks with gradually increasing complexity. Other methods include Monte Carlo methods and intrinsic motivation.
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Foundations of Large Language Models
Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Methods for Mitigating Sparse Rewards