OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learning

Understand

Reinforcement learning has shown promise in learning policies that can solve complex problems.

• However, manually specifying a good reward function can be difficult, especially for intricate tasks.
• Inverse reinforcement learning offers a useful paradigm to learn the underlying reward function directly from expert demonstrations.
• Yet in reality, the corpus of demonstrations may contain trajectories arising from a diverse set of underlying reward functions rather than a single one.