The AI Economist learns dynamic tax policies that optimize equality along with productivity in simulated economies, outperforming alternative tax systems. Read More
Tag Archives: Reinforcement Learning
An AI can simulate an economy millions of times to create fairer tax policy
Deep reinforcement learning has trained AIs to beat humans at complex games like Go and StarCraft. Could it also do a better job at running the economy?
Income inequality is one of the overarching problems of economics. One of the most effective tools policymakers have to address it is taxation: governments collect money from people according to what they earn and redistribute it either directly, via welfare schemes, or indirectly, by using it to pay for public projects. But though more taxation can lead to greater equality, taxing people too much can discourage them from working or motivate them to find ways to avoid paying—which reduces the overall pot.
Getting the balance right is not easy. Read More
Google Teases Large Scale Reinforcement Learning Infrastructure
“The new infrastructure reduces the training time from eight hours down to merely one hour compared to a strong baseline.”
The current state-of-the-art reinforcement learning techniques require many iterations over many samples from the environment to learn a target task. For instance, the game Dota 2 learns from batches of 2 million frames every 2 seconds. The infrastructure that handles RL at this scale should be not only good at collecting a large number of samples, but also be able to quickly iterate over these extensive amounts of samples during training. Read More
A deep learning model achieves super-human performance at Gran Turismo Sport
Over the past few decades, research teams worldwide have developed machine learning and deep learning techniques that can achieve human-comparable performance on a variety of tasks. Some of these models were also trained to play renowned board or videogames, such as the Ancient Chinese game Go or Atari arcade games, in order to further assess their capabilities and performance.
Researchers at University of Zurich and SONY AI Zurich have recently tested the performance of a deep reinforcement learning-based approach that was trained to play Gran Turismo Sport, the renowned car racing video game developed by Polyphony Digital and published by Sony Interactive Entertainment. Their findings, presented in a paper pre-published on arXiv, further highlight the potential of deep learning techniques for controlling cars in simulated environments. Read More
Top 10 Reinforcement Learning Courses & Certifications in 2020
Reinforcement Learning is one of the most in demand research topics whose popularity is only growing day by day. Reinforcement learning (RL) translates to learning by interacting from the surrounding environment. An RL expert learns from experience, rather than being explicitly taught, which is essentially trial and error learning. To understand RL, Analytics Insight compiles the Top 10 Reinforcement Learning Courses and Certifications in 2020. Read More
Deep Reinforcement Learning for Automated Stock Trading
Using reinforcement learning to trade multiple stocks through Python and OpenAI Gym | Presented at ICAIF 2020
Our code is available on Github.
One can hardly overestimate the crucial role stock trading strategies play in investment.
Profitable automated stock trading strategy is vital to investment companies and hedge funds. It is applied to optimize capital allocation and maximize investment performance, such as expected return. Return maximization can be based on the estimates of potential return and risk. However, it is challenging to design a profitable strategy in a complex and dynamic stock market. Read More
An AI Just Beat a Human F-16 Pilot In a Dogfight — Again
In five rounds, an artificially-intelligent agent showed that it could outshoot other AI’s, and a human. So what happens next with AI in air combat?
The never-ending saga of machines outperforming humans has a new chapter. An AI algorithm has again beaten a human fighter pilot in a virtual dogfight. The contest was the finale of the U.S. military’s AlphaDogfight challenge, an effort to “demonstrate the feasibility of developing effective, intelligent autonomous agents capable of defeating adversary aircraft in a dogfight. “ Read More
Neuroevolution of Self-Interpretable Agents
Inattentional blindness is the psychological phenomenon that causes one to miss things in plain sight. It is a consequence of the selective attention in perception that lets us remain focused on important parts of our world without distraction from irrelevant details. Motivated by selective attention, we study the properties of artificial agents that perceive the world through the lens of a self-attention bottleneck. By constraining access to only a small fraction of the visual input, we show that their policies are directly interpretable in pixel space. We find neuroevolution ideal for training self-attention architectures for vision-based reinforcement learning (RL) tasks,allowing us to incorporate modules that can include discrete, non-differentiable operations which are useful for our agent. We argue that self-attention has similar properties as indirect encoding, in the sense that large implicit weight matrices are generated from a small number of key-query parameters, thus enabling our agent to solve challenging vision based tasks with at least 1000x fewer parameters than existing methods. Since our agent attends to only task critical visual hints, they are able to generalize to environments where task irrelevant elements are modified while conventional methods fail. Read More
#image-recognition, #reinforcement-learning, #visionDeepMind’s Newest AI Programs Itself to Make All the Right Decisions
When Deep Blue defeated world chess champion Garry Kasparov in 1997, it may have seemed artificial intelligence had finally arrived. A computer had just taken down one of the top chess players of all time. But it wasn’t to be.
Though Deep Blue was meticulously programmed top-to-bottom to play chess, the approach was too labor-intensive, too dependent on clear rules and bounded possibilities to succeed at more complex games, let alone in the real world. The next revolution would take a decade and a half, when vastly more computing power and data revived machine learning, an old idea in artificial intelligence just waiting for the world to catch up. Read More
Discovering Reinforcement Learning Algorithms
Reinforcement learning (RL) algorithms update an agent’s parameters according to one of several possible rules, discovered manually through years of research. Automating the discovery of update rules from data could lead to more efficient algorithms, or algorithms that are better adapted to specific environments. Although there have been prior attempts at addressing this significant scientific challenge, it remains an open question whether it is feasible to discover alternatives to fundamental concepts of RL such as value functions and temporal-difference learning. This paper introduces a new meta-learning approach that discovers an entire update rule which includes both ‘what to predict’ (e.g. value functions) and ‘how to learn from it’ (e.g. bootstrapping) by interacting with a set of environments. The output of this method is an RL algorithm that we call Learned Policy Gradient (LPG). Empirical results show that our method discovers its own alternative to the concept of value functions. Furthermore it discovers a bootstrapping mechanism to maintain and use its predictions. Surprisingly, when trained solely on toy environments, LPG generalises effectively to complex Atari games and achieves non-trivial performance.This shows the potential to discover general RL algorithms from data. Read More
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