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Found 4 papers, 1 papers with code
Approximate discounting-free policy evaluation from transient and recurrent states
We therefore propose a system of approximators for the bias (specifically, its relative value) from transient and recurrent states.
Examining average and discounted reward optimality criteria in reinforcement learning
In reinforcement learning (RL), the goal is to obtain an optimal policy, for which the optimality criterion is fundamentally important.
A nearly Blackwell-optimal policy gradient method
In this work, we develop a policy gradient method that optimizes the gain, then the bias (which indicates the transient performance and is important to capably select from policies with equal gain).
Average-reward model-free reinforcement learning: a systematic review and literature mapping
Reinforcement learning is important part of artificial intelligence.
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