n South African Computer Journal - Upper bounds on the performance of discretisation in reinforcement learning : research article

Volume 57, Issue 1
  • ISSN : 1015-7999
  • E-ISSN: 2313-7835



Reinforcement learning is a machine learning framework whereby an agent learns to perform a task by maximising its total reward received for selecting actions in each state. The policy mapping states to actions that the agent learns is either represented explicitly, or implicitly through a value function. It is common in reinforcement learning to discretise a continuous state space using tile coding or binary features. We prove an upper bound on the performance of discretisation for direct policy representation or value function approximation.

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