Direct policy gradient methods for reinforcement learning and continuous control problems are a popular approach for a variety of reasons: 1) they are easy to implement without explicit knowledge of the underlying model 2) they are an "end-to-end" approach, directly optimizing the performance metric of interest 3) they inherently allow for richly parameterized policies. A notable drawback is that even in the most basic continuous control problem (that of linear quadratic regulators), these methods must solve a non-convex optimization problem, where little is understood about their efficiency from both computational and statistical perspectives. In contrast, system identification and model based planning in optimal control theory have a much more solid theoretical footing, where much is known with regards to their computational and statistical properties. This work bridges this gap showing that (model free) policy gradient methods globally converge to the optimal solution and are efficient (polynomially so in relevant problem dependent quantities) with regards to their sample and computational complexities.

Siraj Raval programs a virtual robot to do some house cleaning using a technique called Monte Carlo Prediction. In typical Siraj fashion he explains what it is, how it works and how to use it for reinforcement learning.

Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it.

Typically, a RL setup is composed of two components, an agent and an environment. Then environment refers to the object that the agent is acting on (e.g. the game itself in the Atari game), while the agent represents the RL algorithm. The environment starts by sending a state to the agent, which then based on its knowledge to take an action in response to that state. After that, the environment send a pair of next state and reward back to the agent. The agent will update its knowledge with the reward returned by the environment to evaluate its last action.

I should have mentioned that model-based learning allows the agent to plan ahead. For that statement, I am talking about the transition probability T(s', s, a). You are going from current state s to the next state s' after taking action a, and you have to store all the combinations. I will be very appreciated if you can point out the typo lol.

Predictions for Artificial Intelligence in 2018 Positive reinforcement Reinforcement learning takes inspiration from the ways that animals learn how certain behaviors tend to result in a positive or negative outcome. Using this approach, a computer can say, figure out how to navigate a maze by trial and error and then associate the positive outcome--exiting the maze--with the actions that led up to it. This lets a machine learn without instruction or even explicit examples. The idea has been around for decades, but combining it with large (or deep) neural networks provides the power needed to make it work on really complex problems (like the game of Go). Through relentless experimentation, as well as analysis of previous games, AlphaGo figured out for itself how to play the game at an expert level.

Reinforcement learning sits somewhere in between supervised and unsupervised learning. You know the parts of the truth or output, but not the whole truth. Based on that you teach a computer algorithm to perform some action. If right, the action is rewarded; if wrong, the action is punished. Based on this reward system, the computer learns to know whether what it did was right or wrong.

Deep Reinforcement Learning (RL) is used to help airlines improve their business. So, revenue management (RM) is for maximizing revenue for airlines. Revenue management (RM) first used forecasting traffic flows (customer volumes and willingness to pay), and an optimisation procedure that prioritises among customers by selecting optimal availabilities, or prices. But, revenue management (RM) makes many (and unrealistic) assumptions. Deep Reinforcement learning (RL) is an area of deep learning focused on learning, and receiving feedback in order to optimize its predictions.

We propose expected policy gradients (EPG), which unify stochastic policy gradients (SPG) and deterministic policy gradients (DPG) for reinforcement learning. Inspired by expected sarsa, EPG integrates (or sums) across actions when estimating the gradient, instead of relying only on the action in the sampled trajectory. For continuous action spaces, we first derive a practical result for Gaussian policies and quadric critics and then extend it to an analytical method for the universal case, covering a broad class of actors and critics, including Gaussian, exponential families, and reparameterised policies with bounded support. For Gaussian policies, we show that it is optimal to explore using covariance proportional to the matrix exponential of the scaled Hessian of the critic with respect to the actions. EPG also provides a general framework for reasoning about policy gradient methods, which we use to establish a new general policy gradient theorem, of which the stochastic and deterministic policy gradient theorems are special cases. Furthermore, we prove that EPG reduces the variance of the gradient estimates without requiring deterministic policies and with little computational overhead. Finally, we show that EPG outperforms existing approaches on six challenging domains involving the simulated control of physical systems.

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