Generalizing Skills with Semi-Supervised Reinforcement Learning

Understand

Deep reinforcement learning (RL) can acquire complex behaviors from low-level inputs, such as images.

• However, real-world applications of such methods require generalizing to the vast variability of the real world.
• Deep networks are known to achieve remarkable generalization when provided with massive amounts of labeled data, but can we provide this breadth of experience to an RL agent, such as a robot? The robot might continuously learn as it explores the world around it, even while deployed.
• However, this learning requires access to a reward function, which is often hard to measure in real-world domains, where the reward could depend on, for example, unknown positions of objects or the emotional state of the user.