Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Variance-Penalized Reinforcement Learning for Risk-Averse Asset Allocation
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
Policy Iteration for Factored MDPs
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Learning and value function approximation in complex decision processes
Learning and value function approximation in complex decision processes
Least-squares policy iteration
The Journal of Machine Learning Research
Efficient learning of multi-step best response
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Autonomous inter-task transfer in reinforcement learning domains
Autonomous inter-task transfer in reinforcement learning domains
Policy teaching through reward function learning
Proceedings of the 10th ACM conference on Electronic commerce
Value-based policy teaching with active indirect elicitation
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
A general approach to environment design with one agent
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Least absolute policy iteration for robust value function approximation
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Transfer Learning for Reinforcement Learning Domains: A Survey
The Journal of Machine Learning Research
The Kullback-Leibler divergence rate between Markov sources
IEEE Transactions on Information Theory
Using incentive mechanisms for an adaptive regulation of open multi-agent systems
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Persuading agents to act in the right way: An incentive-based approach
Engineering Applications of Artificial Intelligence
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In this paper we study, for the first time explicitly, the implications of endowing an interested party (i.e. a teacher) with the ability to modify the underlying dynamics of the environment, in order to encourage an agent to learn to follow a specific policy. We introduce a cost function which can be used by the teacher to balance the modifications it makes to the underlying environment dynamics, with the learner's performance compared to some ideal, desired, policy. We formulate teacher's problem of determining optimal environment changes as a planning and control problem, and empirically validate the effectiveness of our model.