Reinforcement Learning with Prototypical Representations

Understand

Learning effective representations in image-based environments is crucial for sample efficient Reinforcement Learning (RL).

• Unfortunately, in RL, representation learning is confounded with the exploratory experience of the agent -- learning a useful representation requires diverse data, while effective exploration is only possible with coherent representations.
• Furthermore, we would like to learn representations that not only generalize across tasks but also accelerate downstream exploration for efficient task-specific training.
• To address these challenges we propose Proto-RL, a self-supervised framework that ties representation learning with exploration through prototypical representations.