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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: Exploration by Random Network Distillation (Burda et al., 2018)

We introduce an exploration bonus for deep reinforcement learning methods that is easy to implement and adds minimal overhead to the computation performed. The bonus...

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)

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

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