- See Also
-
Links
- “AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning”, Mathieu et al 2023
- “Job Hunt As a PhD in RL: How It Actually Happens § Reinforcement Learning Reflections”, Lambert 2022
- “Large-Scale Retrieval for Reinforcement Learning”, Humphreys et al 2022
- “Boosting Search Engines With Interactive Agents”, Ciaramita et al 2022
- “Stochastic MuZero: Planning in Stochastic Environments With a Learned Model”, Antonoglou et al 2022
- “Policy Improvement by Planning With Gumbel”, Danihelka et al 2022
- “MuZero With Self-Competition for Rate Control in VP9 Video Compression”, Mandhane et al 2022
- “Procedural Generalization by Planning With Self-Supervised World Models”, Anand et al 2021
- “Mastering Atari Games With Limited Data”, Ye et al 2021
- “Proper Value Equivalence”, Grimm et al 2021
- “Vector Quantized Models for Planning”, Ozair et al 2021
- “Muesli: Combining Improvements in Policy Optimization”, Hessel et al 2021
- “Podracer Architectures for Scalable Reinforcement Learning”, Hessel et al 2021
- “MuZero Unplugged: Online and Offline Reinforcement Learning by Planning With a Learned Model”, Schrittwieser et al 2021
- “Learning and Planning in Complex Action Spaces”, Hubert et al 2021
- “Scaling Scaling Laws With Board Games”, Jones 2021
- “Playing Nondeterministic Games through Planning With a Learned Model”, Willkens & Pollack 2021
- “Visualizing MuZero Models”, Vries et al 2021
- “Combining Off and On-Policy Training in Model-Based Reinforcement Learning”, Borges & Oliveira 2021
- “Improving Model-Based Reinforcement Learning With Internal State Representations through Self-Supervision”, Scholz et al 2021
- “On the Role of Planning in Model-Based Deep Reinforcement Learning”, Hamrick et al 2020
- “The Value Equivalence Principle for Model-Based Reinforcement Learning”, Grimm et al 2020
- “Measuring Progress in Deep Reinforcement Learning Sample Efficiency”, Anonymous 2020
- “Monte-Carlo Tree Search As Regularized Policy Optimization”, Grill et al 2020
- “Continuous Control for Searching and Planning With a Learned Model”, Yang et al 2020
- “Agent57: Outperforming the Human Atari Benchmark”, Puigdomènech et al 2020
- “MuZero: Mastering Atari, Go, Chess and Shogi by Planning With a Learned Model”, Schrittwieser et al 2019
- “Surprising Negative Results for Generative Adversarial Tree Search”, Azizzadenesheli et al 2018
- “TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning”, Farquhar et al 2017
- Sort By Magic
- Wikipedia
- Miscellaneous
- Link Bibliography
See Also
Links
“AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning”, Mathieu et al 2023
AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning
“Job Hunt As a PhD in RL: How It Actually Happens § Reinforcement Learning Reflections”, Lambert 2022
Job Hunt as a PhD in RL: How it Actually Happens § Reinforcement learning reflections
“Large-Scale Retrieval for Reinforcement Learning”, Humphreys et al 2022
“Boosting Search Engines With Interactive Agents”, Ciaramita et al 2022
“Stochastic MuZero: Planning in Stochastic Environments With a Learned Model”, Antonoglou et al 2022
Stochastic MuZero: Planning in Stochastic Environments with a Learned Model
“Policy Improvement by Planning With Gumbel”, Danihelka et al 2022
“MuZero With Self-Competition for Rate Control in VP9 Video Compression”, Mandhane et al 2022
MuZero with Self-competition for Rate Control in VP9 Video Compression
“Procedural Generalization by Planning With Self-Supervised World Models”, Anand et al 2021
Procedural Generalization by Planning with Self-Supervised World Models
“Mastering Atari Games With Limited Data”, Ye et al 2021
“Proper Value Equivalence”, Grimm et al 2021
“Vector Quantized Models for Planning”, Ozair et al 2021
“Muesli: Combining Improvements in Policy Optimization”, Hessel et al 2021
“Podracer Architectures for Scalable Reinforcement Learning”, Hessel et al 2021
“MuZero Unplugged: Online and Offline Reinforcement Learning by Planning With a Learned Model”, Schrittwieser et al 2021
MuZero Unplugged: Online and Offline Reinforcement Learning by Planning with a Learned Model
“Learning and Planning in Complex Action Spaces”, Hubert et al 2021
“Scaling Scaling Laws With Board Games”, Jones 2021
“Playing Nondeterministic Games through Planning With a Learned Model”, Willkens & Pollack 2021
Playing Nondeterministic Games through Planning with a Learned Model
“Visualizing MuZero Models”, Vries et al 2021
“Combining Off and On-Policy Training in Model-Based Reinforcement Learning”, Borges & Oliveira 2021
Combining Off and On-Policy Training in Model-Based Reinforcement Learning
“Improving Model-Based Reinforcement Learning With Internal State Representations through Self-Supervision”, Scholz et al 2021
“On the Role of Planning in Model-Based Deep Reinforcement Learning”, Hamrick et al 2020
On the role of planning in model-based deep reinforcement learning
“The Value Equivalence Principle for Model-Based Reinforcement Learning”, Grimm et al 2020
The Value Equivalence Principle for Model-Based Reinforcement Learning
“Measuring Progress in Deep Reinforcement Learning Sample Efficiency”, Anonymous 2020
Measuring Progress in Deep Reinforcement Learning Sample Efficiency
“Monte-Carlo Tree Search As Regularized Policy Optimization”, Grill et al 2020
“Continuous Control for Searching and Planning With a Learned Model”, Yang et al 2020
Continuous Control for Searching and Planning with a Learned Model
“Agent57: Outperforming the Human Atari Benchmark”, Puigdomènech et al 2020
“MuZero: Mastering Atari, Go, Chess and Shogi by Planning With a Learned Model”, Schrittwieser et al 2019
MuZero: Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
“Surprising Negative Results for Generative Adversarial Tree Search”, Azizzadenesheli et al 2018
Surprising Negative Results for Generative Adversarial Tree Search
“TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning”, Farquhar et al 2017
TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning
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Annotations sorted by machine learning into inferred 'tags'. This provides an alternative way to browse: instead of by date order, one can browse in topic order. The 'sorted' list has been automatically clustered into multiple sections & auto-labeled for easier browsing.
Beginning with the newest annotation, it uses the embedding of each annotation to attempt to create a list of nearest-neighbor annotations, creating a progression of topics. For more details, see the link.
retrieval
self-supervised
planning
Wikipedia
Miscellaneous
-
https://www.furidamu.org/blog/2020/12/22/muzero-intuition/: -
https://www.lesswrong.com/posts/bPa6AzRgGZGmxbq6n/remaking-efficientzero-as-best-i-can: -
https://www.lesswrong.com/posts/mRwJce3npmzbKfxws/efficientzero-how-it-works:View External Link:
https://www.lesswrong.com/posts/mRwJce3npmzbKfxws/efficientzero-how-it-works
Link Bibliography
-
https://arxiv.org/abs/2206.05314#deepmind: “Large-Scale Retrieval for Reinforcement Learning”, -
https://openreview.net/forum?id=0ZbPmmB61g#google: “Boosting Search Engines With Interactive Agents”, -
https://openreview.net/forum?id=bERaNdoegnO#deepmind: “Policy Improvement by Planning With Gumbel”, -
https://arxiv.org/abs/2111.01587#deepmind: “Procedural Generalization by Planning With Self-Supervised World Models”, -
https://arxiv.org/abs/2111.00210: “Mastering Atari Games With Limited Data”, -
https://arxiv.org/abs/2106.10316#deepmind: “Proper Value Equivalence”, -
https://arxiv.org/abs/2106.04615#deepmind: “Vector Quantized Models for Planning”, -
https://arxiv.org/abs/2104.06272#deepmind: “Podracer Architectures for Scalable Reinforcement Learning”, -
https://arxiv.org/abs/2104.06294#deepmind: “MuZero Unplugged: Online and Offline Reinforcement Learning by Planning With a Learned Model”, -
https://arxiv.org/abs/2102.12924: “Visualizing MuZero Models”, -
https://arxiv.org/abs/2011.03506#deepmind: “The Value Equivalence Principle for Model-Based Reinforcement Learning”, -
https://arxiv.org/abs/2102.04881: “Measuring Progress in Deep Reinforcement Learning Sample Efficiency”, -
https://arxiv.org/abs/2006.07430: “Continuous Control for Searching and Planning With a Learned Model”, -
https://deepmind.google/discover/blog/agent57-outperforming-the-human-atari-benchmark/: “Agent57: Outperforming the Human Atari Benchmark”, -
https://arxiv.org/abs/1710.11417: “TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning”,