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RL Weekly 1: Soft Actor-Critic Code Release; Text-based RL Competition; Learning with Training Wheels

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...

Paper Unraveled: A Deeper Look at Experience Replay (Zhang and Sutton, 2017)

Paper Unraveled: A Deeper Look at Experience Replay (Zhang and Sutton, 2017)

Recently experience replay is widely used in various deep reinforcement learning (RL) algorithms, in this paper we rethink the utility of experience replay. It introduces...

Paper Unraveled: Neural Fitted Q Iteration (Riedmiller, 2005)

Paper Unraveled: Neural Fitted Q Iteration (Riedmiller, 2005)

This paper introduces NFQ, an algorithm for efficient and effective training of a Q-value function represented by a multi-layer perceptron. Based on the principle of...

Notes from the ai.x 2018 Conference: Faster Reinforcement Learning via Transfer

Notes from the ai.x 2018 Conference: Faster Reinforcement Learning via Transfer

SK T-Brain hosted the ai.x Conference on September 6th at Seoul, South Korea. At this conference, John Schulman (OpenAI) spoke about faster reinforcement learning via...

Jupyter Notebook extensions to enhance your efficiency

Jupyter Notebook extensions to enhance your efficiency

Jupyter Notebook is a great tool that allows you to integrate live code, equations, visualizations and narrative text into a document. It is used extensively...

Bias-variance Tradeoff in Reinforcement Learning

Bias-variance Tradeoff in Reinforcement Learning

Bias-variance tradeoff is a familiar term to most people who learned machine learning. In the context of Machine Learning, bias and variance refers to the...