A brief overview of Imitation Learning

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.

Author: Zoltán Lőrincz
Medium | SmartLab AI

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