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How can an RL agent successfully solve a task while showcasing versatility of behaviors—a property intuitive to intelligent systems like humans? Introducing Deep Diffusion Policy Gradient (DDiffPG) - our new algorithm for learning multimodal behaviors from scratch! (1/5)

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Zechu Li Reposted

Robot learning in the real world can be expensive and unsafe in human-centric environments. Solution: Construct simulation on the fly and train in it! Excited to share RialTo, led by @marceltornev on learning resilient policies via real-to-sim-to-real policy learning! A 🧵 (1/12)


Zechu Li Reposted

Working with massive parallel simulation like Isaac Gym? Wonder how to learn policies faster and better than PPO? Check out our poster #102 (1:30pm on Thur) #ICML2023 . We have also open sourced the code. 📔Paper: arxiv.org/abs/2307.12983 🧑‍💻Code: github.com/Improbable-AI/…


Zechu Li Reposted

Introducing Decision Diffuser, a conditional diffusion model that outperforms offline RL across standard benchmarks – using only generative modeling training! Decision Diffusers can also combine multiple constraints and skills at test-time. Website: anuragajay.github.io/decision-diffu… 1/5


Zechu Li Reposted

“ElegantRL: Much Much More Stable than Stable-Baseline3” by Xiao-Yang Liu link.medium.com/VJAGmZUH7nb


Zechu Li Reposted

Check out ElegantRL, a lightweight, stable, and efficient deep reinforcement learning library with < 1000 lines of code designed to make our life easier! It also powers FinRL (github.com/AI4Finance-LLC…). towardsdatascience.com/elegantrl-a-li…


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