MOCHA combines weighted-Chebyshev scalarization with an MGDA-style actor-critic and claims O(epsilon^-2 log) sample complexity for finding epsilon-Pareto-stationary policies, with offline KuaiRand experiments.
Adaptive weighted sum method for multiobjective optimization: a new method for pareto front generation,
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Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach
MOCHA combines weighted-Chebyshev scalarization with an MGDA-style actor-critic and claims O(epsilon^-2 log) sample complexity for finding epsilon-Pareto-stationary policies, with offline KuaiRand experiments.