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Search Results for: Reinforcement Learning

Developers Corner
Yugesh Verma

How are behaviour trees used in reinforcement learning?

Behaviour trees are originally developed in the gaming industries that are mainly used for performing actions or sets of actions in a managerial way. We can also use this tree in reinforcement learning.

Developers Corner
Yugesh Verma

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.

Developers Corner
Ram Sagar

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.

Developers Corner
Victor Dey

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.

Developers Corner
Victor Dey

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. 

Developers Corner
Ram Sagar

What Is Model-Free Reinforcement Learning?

“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