Hippocampal Replay for Learning (3 min summary) скачать в хорошем качестве

Hippocampal Replay for Learning (3 min summary)

Using Hippocampal Replay to Consolidate Experiences in Memory-Augmented Reinforcement Learning (Paper ID 38) In-depth video explaining paper (+ bonus future work of Goal-Directed Intrinsic Reward):    • Hippocampal Replay for Learning (Full Leng...   See updated ideas here in RL Fast and Slow:    • Reinforcement Learning Fast and Slow: Goal...   Go-Explore Explanation:    • Go-Explore: Solving Hard Exploration Problems   Paper link: https://openreview.net/forum?id=RAOVI... Code: https://github.com/tanchongmin/Hippoc... #MemARI_2022 Brief description: Traditional Reinforcement Learning (RL) agents have difficulty learning from a sparse reward signal. To overcome this, we use a similar memory augmentation mechanism as Go-Explore, and store the most competent trajectories in memory. In order to enable consistent performance, we use hippocampal replay (preplay to consolidate states, replay to update memory of states) to generate an "exploration highway" to facilitate exploration of good states in the future. Such a method of performing hippocampal replay leads to consistent performance (higher solve rate), and less exploration (higher minimum number of steps to solve). ~~~~~~~~~~~~~~~~~~~~~~~~~ AI and ML enthusiast. Likes to think about the essences behind breakthroughs of AI and explain it in a simple and relatable way. Also, I am an avid game creator. Discord:   / discord   LinkedIn:   / chong-min-tan-94652288   Online AI blog: https://delvingintotech.wordpress.com/. Twitter:   / johntanchongmin   Try out my games here: https://simmer.io/@chongmin