ELSIM: End-to-end learning of reusable skills through intrinsic motivation
This work addresses the challenge of skill transfer and exploration in reinforcement learning for agents, though it appears incremental as it builds on existing mutual information objectives with a novel curriculum algorithm.
The paper tackles the problem of learning reusable skills in reinforcement learning by introducing a hierarchical architecture that learns skills end-to-end through intrinsic motivation, resulting in improved transfer learning and exploration in sparse-reward MuJoCo environments compared to a baseline.
Taking inspiration from developmental learning, we present a novel reinforcement learning architecture which hierarchically learns and represents self-generated skills in an end-to-end way. With this architecture, an agent focuses only on task-rewarded skills while keeping the learning process of skills bottom-up. This bottom-up approach allows to learn skills that 1- are transferable across tasks, 2- improves exploration when rewards are sparse. To do so, we combine a previously defined mutual information objective with a novel curriculum learning algorithm, creating an unlimited and explorable tree of skills. We test our agent on simple gridworld environments to understand and visualize how the agent distinguishes between its skills. Then we show that our approach can scale on more difficult MuJoCo environments in which our agent is able to build a representation of skills which improve over a baseline both transfer learning and exploration when rewards are sparse.