Table of Contents
What are the important components of reinforcement learning?
Beyond the agent and the environment, one can identify four main subelements of a reinforcement learning system: a policy, a reward function, a value function, and, optionally, a model of the environment. A policy defines the learning agent’s way of behaving at a given time.
What is the primary objective of reinforcement learning?
The purpose of reinforcement learning is for the agent to learn an optimal, or nearly-optimal, policy that maximizes the “reward function” or other user-provided reinforcement signal that accumulates from the immediate rewards.
Does reinforcement learning have a place in business applications?
At a 2017 O’Reilly AI conference, Andrew Ng ranked reinforcement learning dead last in terms of its utility for business applications.
Is deep reinforcement learning (RL) data hungry?
Compared to other machine learning methods like supervised learning, transfer learning, and even unsupervised learning, deep reinforcement learning (RL) is incredibly data hungry, often unstable, and rarely the best option in terms of performance.
What’s new in reinforcement learning in 2018?
In 2018, we saw a number of advancements that could make reinforcement learning much more applicable to real-world domains. This includes increased data efficiency and stability, multi-tasking, and the recently introduced Horizon platform for applied RL.
What are the challenges of model-free deep reinforcement learning algorithms?
Model-free deep reinforcement learning (RL) algorithms have been demonstrated on a range of challenging decision making and control tasks. However, these methods typically suffer from two major challenges: very high sample complexity and brittle convergence properties, which necessitate meticulous hyperparameter tuning.