OpenAI Blog
Feb 26, 2019
Spinning Up in Deep RL: Workshop review
On February 2, we held our first Spinning Up Workshop as part of our new education initiative at OpenAI.
Spinning Up in Deep RL
We’re releasing Spinning Up in Deep RL, an educational resource designed to let anyone learn to become a skilled practitioner in deep reinforcement learning. Spinning Up consists of crystal-clear examples of RL code, educational exercises, documentation, and tutorials.
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WoVR: World Models as Reliable Simulators for Post-Training VLA Policies with RL
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V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control
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Trust Your Instincts: Confidence-Driven Test-Time RL for Vision-Language-Action Models
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WorldSample: Closed-loop Real-robot RL with World Modelling
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arXiv AI
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From Classical to Quantum Reinforcement Learning and Its Applications in Quantum Control: A Beginner's Tutorial
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OpenAI Blog
Apr 27, 2016
OpenAI Gym Beta
We’re releasing the public beta of OpenAI Gym, a toolkit for developing and comparing reinforcement learning (RL) algorithms. It consists of a growing suite of environments (from simulated robots to Atari games), and a site for comparing and reproducing results.
OpenAI Blog
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Gathering human feedback
RL-Teacher is an open-source implementation of our interface to train AIs via occasional human feedback rather than hand-crafted reward functions. The underlying technique was developed as a step towards safe AI systems, but also applies to reinforcement learning problems with rewards that are hard to specify.
arXiv Machine Learning
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Coherent Off-Policy Improvement of Large Behavior Models with Learned Rewards
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ProDVI: Programmatic Dynamics Priors for Value Network Initialization
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Self-Distillation Enables Continual Learning
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