Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping

Ng, A. Y., Harada, D., & Russell, S. (1999). Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping: Theory and Application to Reward Shaping. Proceedings of the Sixteenth International Conference on Machine Learning.

Type

Conference paper

Intellectual base

Primary research

Year

1999

DOI

10.5555/645528.657613

Link

https://dl.acm.org/doi/10.5555/645528.657613

Cited from

decision_science

Cited by

1 citation across 1 artifact.

Each citation links to the sentence it supports in the citing article.

Primes

• Reference-Point Dependence
  • The subtraction can leave the optimum untouched while transforming the learning dynamics — reference dependence in a system with no experience of gain or loss whatsoever.

    This sourceProves that potential-based shaping leaves the optimal policy unchanged while altering the learning dynamics.

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