Evaluating Agents without Rewards

Understand

Reinforcement learning has enabled agents to solve challenging tasks in unknown environments.

• However, manually crafting reward functions can be time consuming, expensive, and error prone to human error.
• Competing objectives have been proposed for agents to learn without external supervision, but it has been unclear how well they reflect task rewards or human behavior.
• To accelerate the development of intrinsic objectives, we retrospectively compute potential objectives on pre-collected datasets of agent behavior, rather than optimizing them online, and compare them by analyzing their correlations.