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Title: Planning to Explore via Self-Supervised World Models (Paper Explained)

Playlist:Papers Explained

Author:Yannic Kilcher

Views: 4673.0

Rating: 4.936842

Level: advanced

Length: 27295.0

Type: examples

Topics: state policy action reward game agent value actions states reinforcement

Cluster: policy reward state action agent value just learning actions reinforcement





Selection in the same TOPIC

Author
Link
Rating
Level
UdacitySpeed Scatterplot 2 - Intro to Machine Learning5basic
RAILCS 182: Lecture 21: Part 3: Meta-Learning5intermediate
stanfordonlineLecture 6: Search 2 - A* | Stanford CS221: AI (Autumn 2019)5basic
Yannic KilcherDynamics-Aware Unsupervised Discovery of Skills (Paper Explained)5advanced
The AI EpiphanyDQN - Playing Atari with Deep Reinforcement Learning | RL Paper Explained5advanced
ComputerphileEXTRA BITS: AI Gridworlds - Computerphile4.9932885advanced


Selection in the same CLUSTER

Author
Link
Rating
Level
RAILCS 182: Lecture 14: Part 2: Imitation Learning5intermediate
Yannic KilcherAMP: Adversarial Motion Priors for Stylized Physics-Based Character Control (Paper Explained)5advanced
Graham NeubigCMU Advanced NLP 2021 (22): Reinforcement Learning and Structured Learning Algorithms5advanced
ComputerphileEXTRA BITS: AI Gridworlds - Computerphile4.9932885advanced
Yannic KilcherChip Placement with Deep Reinforcement Learning (Paper Explained)4.9849057intermediate
stanfordonlineStanford CS230: Deep Learning | Autumn 2018 | Lecture 9 - Deep Reinforcement Learning4.9820628intermediate


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Yannic KilcherDreamer v2: Mastering Atari with Discrete World Models (Machine Learning Research Paper Explained)4.9878788advanced
DeepMindReinforcement Learning 1: Introduction to Reinforcement Learning4.9184904advanced
Yannic KilcherDynamics-Aware Unsupervised Discovery of Skills (Paper Explained)5advanced
Yannic KilcherEfficientZero: Mastering Atari Games with Limited Data (Machine Learning Research Paper Explained)4.9764013advanced
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