Multi-task Maximum Entropy Inverse Reinforcement Learning

Understand

Multi-task Inverse Reinforcement Learning (IRL) is the problem of inferring multiple reward functions from expert demonstrations.

• Prior work, built on Bayesian IRL, is unable to scale to complex environments due to computational constraints.
• This paper contributes a formulation of multi-task IRL in the more computationally efficient Maximum Causal Entropy (MCE) IRL framework.
• Experiments show our approach can perform one-shot imitation learning in a gridworld environment that single-task IRL algorithms need hundreds of demonstrations to solve.