Method evidence record
Deep Reinforcement Learning
Deep Reinforcement Learning combines neural networks with reinforcement learning so an agent learns by interacting with an environment, popularised by Mnih and colleagues' 2015 Nature work on human-level Atari control. Instead of learning from a fixed labelled dataset, the agent takes actions, observes rewards, and gradually shapes a policy that maximises long-run return.
Source record
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
Deep Reinforcement Learning (DQN / PPO / A3C)
Taxonomic method record · ml-model / deep-learning
- Mnih, V. et al. (2015). Human-Level Control through Deep Reinforcement Learning. Nature, 518, 529–533. · DOI 10.1038/nature14236
- Schulman, J. et al. (2017). Proximal Policy Optimization Algorithms. arXiv:1707.06347. · URL
Curated claims
Claims persisted in the evidence ledger, each with its own assessment.
No curated claims yet
This view does not invent a claim assessment when the ledger has none.
Related methods
Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.