RL Weekly is a weekly newsletter highlighting important progress in reinforcement learning in research or industry.
RL Weekly 4: Generating Problems with Solutions, Optical Flow with RL, and Model-free Planning
In this issue, we introduce new curriculum learning algorithm by Uber AI Labs, model-free planning algorithm by DeepMind, and optical-flow based control algorithm by Intel Labs and University of Freiburg.
RL Weekly 3: Learning to Drive through Dense Traffic, Learning to Walk, and Summarizing Progress in Sim-to-Real
In this issue, we introduce the DeepTraffic competition from Lex Fridman's MIT Deep Learning for Self-Driving Cars course. We also review a new paper on using SAC to control a four-legged robot, and introduce a website summarizing progress in sim-to-real algorithms.
RL Weekly 2: Tuning AlphaGo, Macro-strategy for MOBA, Sim-to-Real with conditional GANs
In this issue, we discuss hyperparameter tuning for AlphaGo from DeepMind, Hierarchical RL model for a MOBA game from Tencent, and GAN-based Sim-to-Real algorithm from X, Google Brain, and DeepMind.
RL Weekly 1: Soft Actor-Critic Code Release; Text-based RL Competition; Learning with Training Wheels
In this inaugural issue of the RL Weekly newsletter, we discuss Soft Actor-Critic (SAC) from BAIR, the new TextWorld competition by Microsoft Research, and AsDDPG from University of Oxford and Heriot-Watt University.