Abstract: A machine learning system comprises: a set of agents, each having associated processing circuitry and associated memory circuitry, the associated memory circuitry of each agent holding a respective policy for selecting an action in dependence on the agent making an observation of an environment; and a meta-agent having associated processing circuitry and associated memory circuitry. The associated memory circuitry of each agent further holds program code which, when executed by the associated processing circuitry of that agent, causes that agent to update iteratively the respective policy of that agent, each iteration of the updating comprising, for each of a sequence of time steps: making an observation of the environment; selecting and performing an action depending on the observation and the respective policy; and determining a reward in response to performing the selected action, the reward depending on a reward modifier parameter.