Technical Note: \cal Q-Learning
Machine Learning
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Learning to act using real-time dynamic programming
Artificial Intelligence - Special volume on computational research on interaction and agency, part 1
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Algorithm-Directed Exploration for Model-Based Reinforcement Learning in Factored MDPs
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
R-max - a general polynomial time algorithm for near-optimal reinforcement learning
The Journal of Machine Learning Research
Efficient reinforcement learning with relocatable action models
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Potential-based shaping and Q-value initialization are equivalent
Journal of Artificial Intelligence Research
Reinforcement Learning in Finite MDPs: PAC Analysis
The Journal of Machine Learning Research
PAC-MDP learning with knowledge-based admissible models
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Theoretical considerations of potential-based reward shaping for multi-agent systems
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Policy invariance under reward transformations for general-sum stochastic games
Journal of Artificial Intelligence Research
Multi-Task reinforcement learning: shaping and feature selection
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
Dynamic potential-based reward shaping
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Potential-based reward shaping for POMDPs
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Learning potential functions and their representations for multi-task reinforcement learning
Autonomous Agents and Multi-Agent Systems
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Potential-based shaping was designed as a way of introducing background knowledge into model-free reinforcement-learning algorithms. By identifying states that are likely to have high value, this approach can decrease experience complexity--the number of trials needed to find near-optimal behavior. An orthogonal way of decreasing experience complexity is to use a model-based learning approach, building and exploiting an explicit transition model. In this paper, we show how potential-based shaping can be redefined to work in the model-based setting to produce an algorithm that shares the benefits of both ideas.