  We present MEM: Multi-view Exploration Maximization for tackling complex
visual control tasks. To the best of our knowledge, MEM is the first approach
that combines multi-view representation learning and intrinsic reward-driven
exploration in reinforcement learning (RL). More specifically, MEM first
extracts the specific and shared information of multi-view observations to form
high-quality features before performing RL on the learned features, enabling
the agent to fully comprehend the environment and yield better actions.
Furthermore, MEM transforms the multi-view features into intrinsic rewards
based on entropy maximization to encourage exploration. As a result, MEM can
significantly promote the sample-efficiency and generalization ability of the
RL agent, facilitating solving real-world problems with high-dimensional
observations and spare-reward space. We evaluate MEM on various tasks from
DeepMind Control Suite and Procgen games. Extensive simulation results
demonstrate that MEM can achieve superior performance and outperform the
benchmarking schemes with simple architecture and higher efficiency.
