Simplified Temporal Consistency Reinforcement Learning

Understand

Reinforcement learning is able to solve complex sequential decision-making tasks but is currently limited by sample efficiency and required computation.

• To improve sample efficiency, recent work focuses on model-based RL which interleaves model learning with planning.
• Recent methods further utilize policy learning, value estimation, and, self-supervised learning as auxiliary objectives.
• In this paper we show that, surprisingly, a simple representation learning approach relying only on a latent dynamics model trained by latent temporal consistency is sufficient for high-performance RL.