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Found 4 papers, 2 papers with code
Learning Fair Policies in Multi-Objective (Deep) Reinforcement Learning with Average and Discounted Rewards
During this analysis, we notably derive a new result in the standard RL setting, which is of independent interest: it states a novel bound on the approximation error with respect to the optimal average reward of that of a policy optimal for the discounted reward.
Fairness in Preference-based Reinforcement Learning
Toward this objective, we design a new fairness-induced preference-based reinforcement learning or FPbRL.
Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning
As a solution method, we propose a novel neural network architecture, which is composed of two sub-networks specifically designed for taking into account the two aspects of fairness.
Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning with Average and Discounted Rewards
Since learning with discounted rewards is generally easier, this discussion further justifies finding a fair policy for the average reward by learning a fair policy for the discounted reward.
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