Mask-based Latent Reconstruction for Reinforcement Learning
This addresses the challenge of limited experience and high-dimensional inputs in RL for robotics and control applications, representing an incremental improvement over existing representation learning approaches.
The paper tackles the problem of learning effective state representations in deep reinforcement learning from pixels by introducing Mask-based Latent Reconstruction (MLR), a self-supervised method that predicts complete state representations from masked observations. The result shows that MLR significantly improves sample efficiency and outperforms state-of-the-art methods on multiple control benchmarks.
For deep reinforcement learning (RL) from pixels, learning effective state representations is crucial for achieving high performance. However, in practice, limited experience and high-dimensional inputs prevent effective representation learning. To address this, motivated by the success of mask-based modeling in other research fields, we introduce mask-based reconstruction to promote state representation learning in RL. Specifically, we propose a simple yet effective self-supervised method, Mask-based Latent Reconstruction (MLR), to predict complete state representations in the latent space from the observations with spatially and temporally masked pixels. MLR enables better use of context information when learning state representations to make them more informative, which facilitates the training of RL agents. Extensive experiments show that our MLR significantly improves the sample efficiency in RL and outperforms the state-of-the-art sample-efficient RL methods on multiple continuous and discrete control benchmarks. Our code is available at https://github.com/microsoft/Mask-based-Latent-Reconstruction.