Human-level control through deep reinforcement learning
Results and benchmarks
Human-level control through deep reinforcement learning presents a reinforcement learning approach for computer science.
Benchmark evidence is limited
Evidence graph: 2 refs, 1 links.
Utility signals: depth 65/100, grounding 58/100, status medium.
Implementation
No direct implementation yet
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No verified maintained repo yet
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- No direct maintained implementation was found. Use the paper PDF and citation graph to design a baseline reproduction.
- Start from related paper: Learning to see and act.
- Start from this likely method family: Reinforcement learning.
Time to first repro: a few days
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- Estimate is based on paper-only reproduction flow
Reproduction readiness
No repo
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Hardware requirements
- Expect multi-day setup/compute for meaningful reproduction based on current guidance.
Validation caveat
Hugging Face artifacts
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Research context
30,346
Citations
37
References
Tasks
Computer science, Variety (cybernetics), Deep learning, Control (management), Perception, Human–computer interaction, deep Q-network, game-playing agents
Methods
Reinforcement learning, deep reinforcement learning
Domains
Artificial intelligence, Machine learning
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