Unsupervised Perceptual Rewards for Imitation Learning

Understand

Reward function design and exploration time are arguably the biggest obstacles to the deployment of reinforcement learning (RL) agents in the real world.

• In many real-world tasks, designing a reward function takes considerable hand engineering and often requires additional sensors to be installed just to measure whether the task has been executed successfully.
• Furthermore, many interesting tasks consist of multiple implicit intermediate steps that must be executed in sequence.
• Even when the final outcome can be measured, it does not necessarily provide feedback on these intermediate steps.