Vision systems that see and reason about the compositional nature of visual scenes are
fundamental to understanding our world. The complex relations between objects and their …

This paper reviews exploration techniques in deep reinforcement learning. Exploration
techniques are of primary importance when solving sparse reward problems. In sparse …

We introduce Voyager, the first LLM-powered embodied lifelong learning agent in Minecraft
that continuously explores the world, acquires diverse skills, and makes novel discoveries …

Reinforcement learning promises to solve complex sequential-decision problems
autonomously by specifying a high-level reward function only. However, reinforcement …

Much of recent machine learning has focused on deep learning, in which neural network
weights are trained through variants of stochastic gradient descent. An alternative approach …

In recent years, machine learning, especially various deep neural networks, as an emerging
technique for data analysis and processing, has brought novel insights into the development …

A grand challenge in reinforcement learning is intelligent exploration, especially when
rewards are sparse or deceptive. Two Atari games serve as benchmarks for such hard …

Time series prediction with deep learning methods, especially Long Short-term Memory
Neural Network (LSTM), have scored significant achievements in recent years. Despite the …

Over recent years, there has been a rapid development of deep learning (DL) in both
industry and academia fields. However, finding the optimal hyperparameters of a DL model …

Rewards are sparse in the real world and most of today's reinforcement learning algorithms
struggle with such sparsity. One solution to this problem is to allow the agent to create …