The paper introduces a framework for multi-policy multi-objective reinforcement learning that learns a set of Pareto-optimal policies ensuring fairness across diverse user preferences. It proves fair policies remain within the convex coverage set for concave welfare functions like GGF and proposes three algorithms that incorporate non-stationary and stochastic policies to adapt to historical inequities. Empirical results show these methods effectively learn fair policies across multiple domains.