π£ Homework 11 is out!
β° Deadline: July 4th (Ϋ±Ϋ³ ΨͺΫΨ±) 11:59 PM
π https://deeprlcourse.github.io/homeworks/week11
β° Deadline: July 4th (Ϋ±Ϋ³ ΨͺΫΨ±) 11:59 PM
π https://deeprlcourse.github.io/homeworks/week11
deeprlcourse.github.io
HW11: Imitation & Inverse RL - Deep RL Course
This page contains the homework assignment for Week 11 of the Deep Reinforcement Learning course, focusing on Imitation & Inverse RL.
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π£ Homework 12 is out!
β° Deadline: July 4th (Ϋ±Ϋ³ ΨͺΫΨ±) 11:59 PM
π https://deeprlcourse.github.io/homeworks/week12
β° Deadline: July 4th (Ϋ±Ϋ³ ΨͺΫΨ±) 11:59 PM
π https://deeprlcourse.github.io/homeworks/week12
deeprlcourse.github.io
HW12: Offline Methods - Deep RL Course
This page contains the homework assignment for Week 12 of the Deep Reinforcement Learning course, focusing on Offline Methods.
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π£ Richard Sutton's Talk at Reinforcement Learning Conference 2025:
https://youtube.com/watch?v=gEbbGyNkR2U
https://youtube.com/watch?v=gEbbGyNkR2U
YouTube
Rich Sutton, The OaK Architecture: A Vision of SuperIntelligence from Experience - RLC 2025
As AI has become a huge industry, to a large extent it has lost its way. What is needed to get us back on track to true intelligence? We need agents that learn continually. We need world models and planning. We need knowledge that is high-level and learnable.β¦
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π£ Michael Littman's Talk at Reinforcement Learning Conference 2025:
https://www.youtube.com/watch?v=orxCYhb9iDI
https://www.youtube.com/watch?v=orxCYhb9iDI
YouTube
Michael Littman, Efficient RL/AI via Interaction and Teaching - RLC 2025
Splashy RL/AI headlines sometimes obscure a deep truth---we can't really tell if these systems reliably do what we train them to do. In this talk, I assert that theory, that is, mathematical analysis, is a critical conceptual and even practical tool for helpingβ¦
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π£ Peter Dayan's Talk at Reinforcement Learning Conference 2025:
https://www.youtube.com/watch?v=2OOGxkqM2oI
https://www.youtube.com/watch?v=2OOGxkqM2oI
YouTube
Peter Dayan, The Marriage of Natural & Engineered Reinforcement Learning - RLC 2025
Natural and engineered reinforcement learning (RL) have been on- and off-again bedfellows for more than 70 years. There are many themes that are actually central to both, but they are often couched and characterized in different terms. This is true of debatesβ¦
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π£ Stanford Spring 2025 Deep Reinforcement Learning Course:
π©π»βπ« Prof. Chelsea Finn
π https://cs224r.stanford.edu
π₯ https://www.youtube.com/watch?v=EvHRQhMX7_w&list=PLoROMvodv4rPwxE0ONYRa_itZFdaKCylL
π©π»βπ« Prof. Chelsea Finn
π https://cs224r.stanford.edu
π₯ https://www.youtube.com/watch?v=EvHRQhMX7_w&list=PLoROMvodv4rPwxE0ONYRa_itZFdaKCylL
YouTube
Stanford CS224R Deep Reinforcement Learning | Spring 2025 | Lecture 1: Class Intro
View course details: https://online.stanford.edu/courses/xcs224r-deep-reinforcement-learning
April 2, 2025
This lecture covers:
β’ Class introduction
β’ Markov Decisions Processes
β’ Why study deep reinforcement learning?
β’ Intro to modeling behavior and reinforcementβ¦
April 2, 2025
This lecture covers:
β’ Class introduction
β’ Markov Decisions Processes
β’ Why study deep reinforcement learning?
β’ Intro to modeling behavior and reinforcementβ¦
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π£ DRL2025 Final Feedback Form
Thank you for being part of the Deep Reinforcement Learning course last year! We'd really appreciate your thoughts on the course. This form is:
βοΈ Completely anonymous
βοΈ All questions are optional
π https://forms.gle/v1xjBKbAUR4f5weU9
Your feedback truly helps us make the course better π‘
Thank you for being part of the Deep Reinforcement Learning course last year! We'd really appreciate your thoughts on the course. This form is:
βοΈ Completely anonymous
βοΈ All questions are optional
π https://forms.gle/v1xjBKbAUR4f5weU9
Your feedback truly helps us make the course better π‘
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