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Everything about MuZero
Game-playing artificial intelligence
MuZero is a reinforcement learning algorithm developed by DeepMind that learns a model of an environment for planning without being given its rules or dynamics. It was introduced in a 2019 preliminary paper and described in a 2020 Nature paper. MuZero was evaluated on Go, chess, shogi, and Atari games, where it achieved performance comparable to or better than previous systems in the reported experiments. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: MuZeroA 5-minute orientation
- Skim a recent paper: EvoPlay-MuZero Hybrid Framework Incorporating a Dual-Peptide Bridging Strategy for Adjunctive Therapy in Alzheimer’s DiseaseInfluential work since 2022
- Read the landmark paper: Towards Autonomous Defense of SDN Networks Using MuZero Based Intelligent AgentsCited 13 times
- Go sideways: Timothy LillicrapA closely related topic
- Go sideways: AlphaZeroA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Towards Autonomous Defense of SDN Networks Using MuZero Based Intelligent Agents
Orchestrating Smart Grid Demand Response Operations With URLLC and MuZero Learning
Autonomous solution for Controller Placement Problem of Software-Defined Networking using MuZero based intelligent agents
MiniZero: Comparative Analysis of AlphaZero and MuZero on Go, Othello, and Atari Games
Securing SDN controller placement with MuZero and blockchain-based smart contracts
Multiagent Gumbel MuZero: Efficient Planning in Combinatorial Action Spaces
M0RV Model: Advancing the MuZero Algorithm Through Strategic Data Optimization Reuse and Value Function Refinement
Demystifying MuZero Planning: Interpreting the Learned Model
🆕 Influential since 2022
📚 Books worth reading
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🔗 Related topics
- Timothy Lillicrap Timothy P. Lillicrap is a Canadian neuroscientist and artificial intelligence (AI) researcher, adjunct professor at University College London, and staff…
- AlphaZero AlphaZero is a computer program developed by artificial intelligence research company DeepMind to master the games of chess, shogi and go. This algorithm…
- AlphaGo Zero AlphaGo Zero is a version of DeepMind's Go software AlphaGo. AlphaGo's team published an article in Nature in October 2017 introducing AlphaGo Zero, a…
- Self-play Self-play is a technique for improving the performance of reinforcement learning agents. Intuitively, agents learn to improve their performance by playing…
- Elmo (shogi engine) elmo (stylized as elmo, a blend of elastic and monkey) is a computer shogi evaluation function and book file (joseki) created by Makoto Takizawa (瀧澤誠)…
- Evaluation function An evaluation function, also known as a heuristic evaluation function or static evaluation function, is a function used by game-playing computer programs…
- Ladder (Go) In the game of Go, a ladder (四丁, シチョウ, shichō; Chinese: 征子, romanized: zhēng zǐ) is a basic sequence of moves in which an attacker pursues a group in atari…
- Deep reinforcement learning Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem…