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Reinforcement Learning (RL) is known to be often unsuccessful in environmentswith sparse extrinsic rewards. A possible countermeasure is to endow RL agentswith an intrinsic reward function, or 'intrinsic motivation', which rewards theagent based on certain features of the current sensor state. An intrinsicreward function based on the principle of empowerment assigns rewardsproportional to the amount of control the agent has over its own sensors. Weimplemented a variation on a recently proposed intrinsically motivated agent,which we refer to as the 'curious' agent, and an empowerment-inspired agent.The former leverages sensor state encoding with a variational autoencoder,while the latter predicts the next sensor state via a variational informationbottleneck. We compared the performance of both agents to that of an advantageactor-critic baseline in four sparse reward grid worlds. Both the empowermentagent and its curious competitor seem to benefit to similar extents from theirintrinsic rewards. This provides some experimental support to the conjecturethat empowerment can be used to drive exploration.

Experimental Evidence that Empowerment May Drive Exploration in Sparse-Reward Environments