Reinforcement learning (RL) is one of the most interesting areas of machine learning, where an agent interacts with an environment by following a policy. In each state of the environment, it takes action based on the policy, and as a result, receives a reward and transitions to a new state. The goal of RL is to learn an optimal policy which maximizes the long-term cumulative rewards. Here’s a brief overview of Imitation Learning.
For further reading
- @AI News; Generative adversarial imitation learning (GAIL)
- @AI News: Deep reinforcement imitation learning