GAIL

Generative Adversarial Imitation Learning — an algorithm that combines imitation learning with adversarial training. A discriminator learns to distinguish expert demonstrations from policy rollouts; the policy is then trained to fool the discriminator via RL. GAIL learns reward functions implicitly and achieves expert-level performance without hand-designed rewards.

See this in practice: our real-world evals →