Reinforcement Learning papers at NeurIPS 2021

This was my first time at NeurIPS as a full-time Research Engineer, and really enjoyed the dedicated time to discover new papers. Below are some notes on the Reinforcement Learning papers that I enjoyed the most. 1. Automatic Data Augmentation for Generalisation in Reinforcement Learning [arXiv, GitHub] TL;DR. Proposes a theoretically motivated way of using… Continue reading Reinforcement Learning papers at NeurIPS 2021

A brief summary of challenges in Multi-agent RL

Deep reinforcement learning (DRL) algorithms have shown significant success in recent years, surpassing human performance in domains ranging from Atari, Go and no-limit poker [1]. The resemblance of its underlying mechanics to human learning, promises even greater results in real-life applications.Given that many real-world problems involve environments with a large number of learning agents, a… Continue reading A brief summary of challenges in Multi-agent RL