The main idea of the ‘Collect & Infer’ paradigm is to re-think data-efficient reinforcement learning using clear separation of data collection and exploitation into two distinct bet connected processes
Maze supports scalable SOTA algorithms capable of handling multi-agent and hierarchical RL settings with dictionary action and observation spaces
AI Economist combines machine learning and AI-driven economic simulation to overcome current challenges.
The gym is an open-source toolkit for developing and comparing reinforcement learning algorithms. What makes it easier to work with is that it makes it easier to structure your environment using only a few lines of code and compatible with any numerical computation library, such as TensorFlow or Theano.
Model-based Reinforcement Learning (MBRL) for continuous control is an area of research investigating machine learning agents explicitly modelling themselves by interacting with the world. MBRL can learn rapidly from a limited number of trials and enables programmers to integrate specialized domain knowledge into the learning agent about how the world environment works. The library MBRL-Lib is an Open-source Python Library created to provide shared foundations, tools, abstractions, and evaluations for continuous-action MBRL.
Multi-agent reinforcement learning explores how artificial agents interact with one another and their environment.
The cost of a Tesla P100 GPU is around $6,000, providing this evidence will take an unreasonably long time as it is prohibitively expensive to have multiple GPUs
Arranging ‘billions’ of components on a tiny surface area of a computer chip is a complicated process. It calls for precise decision-making at every step
In 2017, Google researchers announced Transformers to the world in a NIPS paper titled “Attention is All You Need”. The self-attention mechanism-based novel neural network
DQN or Deep-Q Networks were first proposed by DeepMind back in 2015 in an attempt to bring the advantages of deep learning to reinforcement learning(RL),
“Researchers seek new and more stimulating environments to tackle RL challenges with AndroidEnv.” Alphabet Inc.’s DeepMind has recently introduced AndroidEnv, an open-source platform for Reinforcement
“Model-based methods rely on planning as their primary component, while model-free methods primarily rely on learning.” Sutton& Barto, Reinforcement Learning: An Introduction In the context
“Specifying a reward function can be one of the trickiest parts of applying RL to a problem.” DeepMind At the heart of a successful reinforcement
TensorForce is an open-source library for Reinforcement Learning, built on the top of the TensorFlow library. Python 3 is required for leveraging this deep RL
Machine learning (ML) can handle many complex tasks than just output singular decisions based on a labelled training dataset. Reinforcement learning (RL), a subset of
Last year, Walmart scrapped its ambitious plan to deploy inventory tracking robots. The retail giant has ended its contract with Bossa Nova Robotics citing “different
Adversarial inputs, also known as machine learning’s optical illusions, are inputs to the model an attacker has intentionally designed to confuse the algorithm into making
This post is in continuation with our previous article about Alchemy, the very first benchmark on meta-Reinforcement Learning. Deepmind with the University of London has
Finding a good reward function for optimal policy in reinforcement learning is often challenging, and Inverse Reinforcement Learning(IRL) handles this limitation very well. In IRL,
CompilerGym is a python toolkit by Facebook. It is a reinforcement learning package for compiler optimization problems. This framework’s motivation is that compilers’ decisions are
“Beyond the cost of a robot, there are many design choices in choosing how to set-up the algorithm and the robot.” Levine et al., From
Facebook ReAgent, previously known as Horizon is an end-to-end platform for using applied Reinforcement Learning in order to solve industrial problems. The main purpose of
Recently, a team of researchers from DeepMind and University College London have released a principled benchmark for meta-reinforcement learning (meta-RL) research, known as Alchemy. The
The Association of Data Scientists (AdaSci), a global professional body of data science and ML practitioners, is holding a full-day workshop on building games using
Reinforcement learning (RL) algorithm powers the brains of walking robots and AI chess grandmasters. The algorithm uses neat tricks (policies) that hunt targets by rewarding
Preface First, let’s discuss all the buzzwords, and then we will move to the implementation part where we code a starter project in stock market
Deep Abstract Q-Network can be considered an advancement of traditional deep Q-learning where, to an extent, it can enable the reinforcement learning agent to get trained in high-dimensional domains.
Reinforcement learning is built on the mathematical foundations of the Markov decision process (MDP). It’s critical to compute an optimal policy in reinforcement learning, and
“Reinforcement Learning is used on many physical systems, but will it work in scenarios where it is hard to press the reset button?” Alphabet Inc.’s
“We want AIs to make decisions, and reinforcement learning is the study of how to make decisions.” — Akshay Krishnamurthy, Principal Researcher at Microsoft The
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