Policy-Based Reward Shaping for Accelerated and Robust Reinforcement Learning

Cheng Wang
Reinforcement learning (RL) has recently achieved great successes in areas such as video games, robotics, and the game of Go. However, a number of challenges remain when it comes to applying RL to real-world sequential decision problems. Reward signals from real systems are often sparse, delayed, or noisy, which can significantly slow down the learning process. Compounding this issue with limited data for training, it can become very difficult to learn effective and robust control...
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