This quick guide focuses on the basics of reinforcement learning (RL) and how GPUs enable accelerated performance for RL.
To give a quick insight on why GPUs matter so much in today's world:
GPUs are great for achieving faster performance using parallel computing architecture. They are designed to run 1000s of parallel threads and are thus also known as SIMD architecture i.e. Single Instruction Multi Data.
A simple example for SIMD would be rendering a game scene on a screen. GPUs using 1000s of cores to render each pixel in parallel. The instruction to render a pixel is same while the data on each pixel is different.
GPUs are finding a great use in Deep learning and Machine learning applications today. But sometimes it can be really frustrating to figure out the best GPUs for deep learning.
So first of all, what really is Reinforcement Learning?
Reinforcement learning is a type of machine learning that provides a framework for solving problems in ways that are similar to the way humans would solve them. It is the machine equivalent of trial and error. The goal is to maximize the amount of reward received by repeatedly attempting different actions.
The use cases for reinforcement learning are wide-ranging, and can be used to solve problems in domains such as healthcare, marketing, traffic management, robotics, education and more. Reinforcement learning is machine learning with experience.
Image source
The most common algorithm for reinforcement learning is called Q-learning which simulates a software agent who has to make a decision or take a course of action in each state.
Open source toolkits such as Open AI Gym can be used for developing and comparing reinforcement learning algorithms.
Image source
Reinforcement Learning uses a reward signal to learn. Its aim is to explore all the possible cases in an environment to learn which actions can help it maximize the total reward collected over time. This exploration is performed by an RL agent.
GPUs in Deep Learning & Reinforcement Learning
Most people think of GPUs as something that is only used for gaming or video editing, but in recent years they have taken on a new role in AI.
GPUs work better than CPUs when it comes to deep learning neural networks and Reinforcement learning because they can process more data at once with less power consumption. This is mainly due to their parallel processing abilities - meaning they can do more calculations at the same time.
CPU vs GPU performance example:
Image source
Deep learning is a type of machine learning where neural networks are used to make inferences about data. These neural networks are computationally demanding because they contain many layers. With the help of GPUs, deep neural nets can be trained much faster than before, which has led to an exponential increase in their use for classification and regression problems.
GPUs are great at matrix multiplications and Deep neural nets have to perform thousands of matrix multiplication tasks during the algorithm training process, thus making GPUs a great fit for them.
A GPU-powered reinforcement learner is a type of machine learning agent that runs the RL experiments on a GPU and tries to learn how to maximise its expected reward by interacting with an environment in which it receives rewards or punishments based on its actions.
Image source
GPUs are also used for applications such as auto-driving, analytics, or any other application that needs to process large amounts of data in parallel where previously it was too high in cost to use them for these purposes.
Benefits of Using GPUs for Deep Learning Applications
GPUs have been proven to be the most efficient processing hardware for deep learning applications. Over the past few years, the use of GPUs for deep learning has been on the rise because of many benefits that they provide. These advantages include high-quality training and fast processing times, as well as less overall cost of experimentation.
GPUs are used because they have more cores, which allows them to process more data at a time and provides better performance. This makes them a great fit for deep learning applications.
GPU’s architecture allows it to learn more quickly and with less training data, making it an ideal option for reinforcement learning as well as supervised and unsupervised machine learning.
What are the Best GPUs for Reinforcement Learning?
A GPU (Graphics Processing Unit) executes complex algorithms efficiently by offering high bandwidth and low latency for its memory access, thus making them the fastest general-purpose compute device.
The NVIDIA Tesla V100 is one of the best GPU for reinforcement learning. It is capable of hosting multiple computational graphs and can be scaled almost linearly up to 8 GPU clusters.
The accelerating factor for deep learning frameworks such as Caffe2, Pytorch, and TensorFlow is their ability to make use of GPU acceleration to achieve better performance.
Conclusion:
Reinforcement learning is a process of trial and error by performing a lot of attempts to maximize reward and thus learn the best actions to perform. GPUs enable faster processing for Reinforcement learning by performing these actions in parallel using its parallel computing architecture.
If you are a deep learning or machine learning engineer then you'd know that GPU computing is very costly on cloud.
We understand your pain.
So to democratize GPU computing access, we built Q Blocks, a decentralized computing platform that enables 50-80% cost efficient GPU computing for Machine learning and Deep learning workloads. 😀
Top comments (0)