Search Results for: "Reinforcement Learning"

An Introductory Guide to Meta Reinforcement Learning (Meta-RL)

Meta Reinforcement learning(Meta-RL) can be explained as performing meta-learning in the field of reinforcement learning. where including meta-learning models in reinforcement learning we can grow the model to perform a variety of tasks.

What Happened in Reinforcement Learning in 2021

2021 saw innovations in the reinforcement learning space in the robotics, gaming , sequential decision making space amidst growing curiosity among students and professionals.

Facebook Wants To Make Reinforcement Learning Easier

A versatile and simple library for sequential agent learning, including reinforcement learning

All Hail The King of Reinforcement Learning, DeepMind

All Hail The King of Reinforcement Learning, DeepMind

Besides reinforcement learning, DeepMind also looks at other fundamental areas like symbolic AI and population-based training.

DeepMind Introduces Reinforcement Learning Lecture Series 2021, Here’s What You Can Learn

The series comprises 13 lectures covering the fundamentals of reinforcement learning and planning in sequential decision problems before progressing to more advanced topics and modern deep RL algorithms.

Are Tech Firms Investing More In Reinforcement Learning Research?

Reinforcement learning has become a priority for tech companies.

Top Exploration Methods In Reinforcement Learning

Researchers from Mila AI Institute, Quebec, in their survey, have departed from the traditional categorisation of RL exploration methods and given a treatise into RL exploration methods.

DeepMind Wants To Change How Reinforcement Learning ‘Collect & Infer’

DeepMind Wants To Change How Reinforcement Learning ‘Collect & Infer’

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

Inside Maze, A New Framework for Applied Reinforcement Learning

Inside Maze, A New Framework for Applied Reinforcement Learning

Maze supports scalable SOTA algorithms capable of handling multi-agent and hierarchical RL settings with dictionary action and observation spaces

Economic Policies

Can Reinforcement Learning Be Used For Better Economic Policies

AI Economist combines machine learning and AI-driven economic simulation to overcome current challenges.

Exploring Panda Gym: A Multi-Goal Reinforcement Learning Environment

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.

Complete Guide To MBRL: Python Tool For Model-Based Reinforcement Learning

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.