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This paper argues that representation learning, not model-based planning, is the key to scalable multitask deep reinforcement learning. It introduces MR.Q, a simple model-free algorithm with auxiliary predictive objectives that outperforms prior world-model-based methods across diverse continuous control tasks.
A unified Python framework using PPO-based deep reinforcement learning for optimizing HVAC control with economizer logic and CO2-constrained ventilation is presented, showing improved energy efficiency and temperature stability over traditional PID controllers.
Hugging Face offers a deep reinforcement learning course with practical exercises, now in low-maintenance state but still a useful resource for learning theory and hands-on DRL.
OpenAI announces the completion of its Fall 2018 Fellows program and celebrates the fellows' research contributions. The organization also open-sourced part of the fellowship curriculum, including 'Spinning up in Deep RL,' an educational resource for learning reinforcement learning.
OpenAI held its first Spinning Up in Deep RL Workshop on February 2, engaging ~90 in-person participants and ~300 livestream viewers to provide education in deep RL, robotics, and AI safety through talks, mentorship, and hands-on projects.
OpenAI released 'Spinning Up in Deep RL,' an educational toolkit featuring introductory materials, curated paper lists, and clean standalone implementations of key RL algorithms (VPG, TRPO, PPO, DDPG, TD3, SAC) designed to help newcomers learn deep reinforcement learning from scratch.