Aspects provided herein are relevant to machine learning techniques, including decomposing single-agent reinforcement learning problems into simpler problems addressed by multiple agents. Actions proposed by the multiple agents are then aggregated using an aggregator, which selects an action to take with respect to an environment. Aspects provided herein are also relevant to a hybrid reward model.