Meta-Inverse Reinforcement Learning with Probabilistic Context Variables

Understand

Providing a suitable reward function to reinforcement learning can be difficult in many real world applications.

• While inverse reinforcement learning (IRL) holds promise for automatically learning reward functions from demonstrations, several major challenges remain.
• First, existing IRL methods learn reward functions from scratch, requiring large numbers of demonstrations to correctly infer the reward for each task the agent may need to perform.
• Second, existing methods typically assume homogeneous demonstrations for a single behavior or task, while in practice, it might be easier to collect datasets of heterogeneous but related behaviors.