Sim2Real – Using Simulation to Train Real-Life Grasping Robots

Grasping real-world objects is considered one of the more iconic examples of the current limits of machine intelligence. While humans can easily grasp and pick up objects they’ve never seen before, even the most advanced robotic arms can’t manipulate objects that they weren’t trained to handle. Recent developments in reinforcement learning (RL) have allowed for […]

Go-Explore – RL Algorithm for Exploration Problems – Solving Montezuma’s Revenge

Uber has released a blog post describing Go-Explore, a new Reinforcement Learning (RL) algorithm to deal with hard exploration problems. These problems are characterized by the lack (or sparsity) of external feedback, which makes it difficult for the algorithm to learn how to operate. One of the popular testbeds for RL algorithms is Atari Games, […]

Curiosity-Driven Learning – Exploration By Random Network Distillation

In recent years, Reinforcement Learning has proven itself to be a powerful technique for solving closed tasks with constant rewards, most commonly games. A major challenge in the field remains training a model when external feedback (reward) to actions is sparse or nonexistent. Recent models have tried to overcome this challenge by creating an intrinsic […]