Theoretical and empirical analysis of reward shaping in reinforcement learning
2009 International Conference on Machine Learning and Applications, 2009•ieeexplore.ieee.org
Reinforcement learning suffers scalability problems due to the state space explosion and the
temporal credit assignment problem. Knowledge-based approaches have received a
significant attention in the area. Reward shaping is a particular approach to incorporate
domain knowledge into reinforcement learning. Theoretical and empirical analysis of this
paper reveals important properties of this principle, especially the influence of the reward
type, MDP discount factor, and the way of evaluating the potential function on the …
temporal credit assignment problem. Knowledge-based approaches have received a
significant attention in the area. Reward shaping is a particular approach to incorporate
domain knowledge into reinforcement learning. Theoretical and empirical analysis of this
paper reveals important properties of this principle, especially the influence of the reward
type, MDP discount factor, and the way of evaluating the potential function on the …
Reinforcement learning suffers scalability problems due to the state space explosion and the temporal credit assignment problem. Knowledge-based approaches have received a significant attention in the area. Reward shaping is a particular approach to incorporate domain knowledge into reinforcement learning. Theoretical and empirical analysis of this paper reveals important properties of this principle, especially the influence of the reward type, MDP discount factor, and the way of evaluating the potential function on the performance.
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