Reinforcement Learning w

Deep reinforcement learning with double Q-Learning

We first show that the recent DQN algorithm, which combines Q-learning with a deep neural network, suffers from substantial overestimations in some games.

Reinforcement Learning With Human Advice: A Survey - Frontiers

In this paper, we provide an overview of the existing methods for integrating human advice into a reinforcement learning process.

Visual Reinforcement Learning with Imagined Goals

In this post, we discuss reinforcement learning algorithms that can be used to learn multiple different tasks simultaneously, without additional human ...

Reinforcement Learning with Augmented Data

To this end, we present Reinforcement Learning with Augmented Data (RAD), a simple plug-and-play module that can enhance most RL algorithms. We perform the ...

Machine Learning for Kids

An educational tool for teaching kids about machine learning, by letting them train a computer to recognise text ... with it in tools like Scratch.

Accelerating Reinforcement Learning with Learned Skill Priors

We propose a deep latent variable model that jointly learns an embedding space of skills and the skill prior from offline agent experience.

From Neural Networks to Reinforcement Learning to Game Theory

Once a generative AI model is effectively trained with a diverse and representative dataset, it can be expected to generate reliable outputs.

Reinforcement learning with human feedback (RLHF) for LLMs

RLHF is a technique where AI improves by learning directly from human feedback. This way, you enrich AI's learning process with real human insights.

Fast reinforcement learning with generalized policy updates - PNAS

The specific way we do so is through a generalization of two fundamental operations in reinforcement learning: policy improvement and policy ...

Efficiently Initializing Reinforcement Learning With Prior Policies

With the above in mind, in “Jump-Start Reinforcement Learning” (JSRL), we introduce a meta-algorithm that can use a pre-existing policy of any ...

MorvanZhou/Reinforcement-learning-with-tensorflow - GitHub

Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学 - MorvanZhou/Reinforcement-learning-with-tensorflow.

Reinforcement learning with artificial microswimmers - Science

We combine real-world artificial active particles with machine learning algorithms to explore their adaptive behavior in a noisy environment with reinforcement ...

5 Free Courses on Reinforcement Learning

Reinforcement learning (RL) is a subfield of machine learning where an agent learns to make decisions by interacting with its environment ...

Applying Reinforcement Learning on Real-World Data with Practical ...

Reinforcement learning is a powerful tool in AI in which virtual or physical agents learn to optimize their decision making to achieve long-term goals.

Experimental quantum speed-up in reinforcement learning agents

An important paradigm within artificial intelligence is reinforcement learning, where decision-making entities called agents interact with ...

Deep Q Learning w/ DQN - Reinforcement Learning p.5 - YouTube

Hello and welcome to the first video about Deep Q-Learning and Deep Q Networks, or DQNs. Deep Q Networks are the deep learning/neural ...

CS 224R Deep Reinforcement Learning

This course is about algorithms for deep reinforcement learning – methods for learning behavior from experience, with a focus on practical algorithms.

Journal of Machine Learning Research

Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity ... Yinghua Yao, Yuangang Pan, Jing Li, Ivor W. Tsang, Xin ...

What is reinforcement learning from human feedback (RLHF)?

RLHF is a machine learning approach that combines reinforcement learning techniques, such as rewards and comparisons, with human guidance to train an ...

Machine Learning and AI with Python - Harvard Online Courses

In Machine Learning and AI with Python, you will explore the most basic algorithm as a basis for your learning and understanding of machine learning: decision ...