Reinforcement Learning using Guided Observability

Understand

Due to recent breakthroughs, reinforcement learning (RL) has demonstrated impressive performance in challenging sequential decision-making problems.

• However, an open question is how to make RL cope with partial observability which is prevalent in many real-world problems.
• Contrary to contemporary RL approaches, which focus mostly on improved memory representations or strong assumptions about the type of partial observability, we propose a simple but efficient approach that can be applied together with a wide variety of RL methods.
• Our main insight is that smoothly transitioning from full observability to partial observability during the training process yields a high performance policy.