Reinforcement Learning with Prediction-Based Rewards
We’ve developed Random Network Distillation (RND), a prediction-based method for encouraging reinforcement learning agents to explore their environments through curiosity, which for the first time exceeds average human performance on Montezuma’s Revenge. Learn more: https://blog.openai.com/reinforcement-learning-with-prediction-based-rewards/
Видео Reinforcement Learning with Prediction-Based Rewards канала OpenAI
Видео Reinforcement Learning with Prediction-Based Rewards канала OpenAI
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
Neural Network Learns to Play Snake
Deep Learning Cars
Montezuma's Revenge 11300 Scores with OpenAI Random Network Distillation By 4 Titan Xp
Multi-Agent Hide and Seek
Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks
This AI Senses Humans Through Walls 👀
MarI/O - Machine Learning for Video Games
Google DeepMind's Deep Q-learning playing Atari Breakout
Google's Deep Mind Explained! - Self Learning A.I.
AI learns to play snake using Genetic Algorithm and Deep learning
Machine Learning Tetris
Deep Q Network learning to play Galaxian (cleaner encode)
Deep Reinforcement Learning in Pac-man
Intrinsically Motivated Goal Exploration, Automated Curriculum Learning and Emergence of Tool Use
Reinforcement Learning Tutorial | Reinforcement Learning Example Using Python | Edureka
Operant conditioning: Positive-and-negative reinforcement and punishment | MCAT | Khan Academy
AI Learns to Park - Deep Reinforcement Learning
Curiosity-Driven Learning: AI agents exploring without looking at any scores
Reinforcement Learning in practice. AI playing games. Breakout