A Geometric Approach to Multi-Criterion Reinforcement Learning

Shie Mannor, Nahum Shimkin; 5(Apr):325--360, 2004.


We consider the problem of reinforcement learning in a controlled Markov environment with multiple objective functions of the long-term average reward type. The environment is initially unknown, and furthermore may be affected by the actions of other agents, actions that are observed but cannot be predicted beforehand. We capture this situation using a stochastic game model, where the learning agent is facing an adversary whose policy is arbitrary and unknown, and where the reward function is vector-valued. State recurrence conditions are imposed throughout. In our basic problem formulation, a desired target set is specified in the vector reward space, and the objective of the learning agent is to approach the target set, in the sense that the long-term average reward vector will belong to this set. We devise appropriate learning algorithms, that essentially use multiple reinforcement learning algorithms for the standard scalar reward problem, which are combined using the geometric insight from the theory of approachability for vector-valued stochastic games. We then address the more general and optimization-related problem, where a nested class of possible target sets is prescribed, and the goal of the learning agent is to approach the smallest possible target set (which will generally depend on the unknown system parameters). A particular case which falls into this framework is that of stochastic games with average reward constraints, and further specialization provides a reinforcement learning algorithm for constrained Markov decision processes. Some basic examples are provided to illustrate these results.


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