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Q-learning is a reinforcement learning algorithm that trains an agent to assign values to its possible actions based on its current state, without requiring a model of the environment (model-free). It can handle problems with stochastic transitions and rewards without requiring adaptations.
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Explore the main themes, entities and connections around Q-learning. Start with the topic map, then use the sections below for research and deeper semantic analysis.
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learning state displaystyle action algorithm agent value reward values reinforcement function time factor rewards current used discount initial policy rate
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Q-learning | is a | reinforcement learning algorithm that trains an agent to assign values to its possible actions based on its current state | 0.90 | text |
| Q-learning | is a | iterative algorithm | 0.90 | text |
| Q-learning | is a | off-policy reinforcement learning algorithm | 0.90 | text |
| Q-learning | is a | alternative implementation of the online Q-learning algorithm | 0.90 | text |
| Q-learning | is a | variant of Q-learning which seeks to model the distribution of returns rather than the expected return of each action | 0.90 | text |
| a neural network is used to represent Q | instance of | Reinforcement learning is unstable or divergent when a nonlinear function approximator | 0.80 | text |
| Wire-fitted Neural Network Q-Learning | instance of | there are adaptations of Q-learning that attempt to solve this problem | 0.80 | text |
| Q-learning | related to Double Q-learning | Because | 0.60 | section |
| Q-learning | related to Double Q-learning | Double Q-learning | 0.60 | section |
| Q-learning | related to Double Q-learning | In | 0.60 | section |
| Q-learning | related to Double Q-learning | The | 0.60 | section |
| Q-learning | related to External links | Watkins | 0.60 | section |
These clusters group vocabulary that occurs around closely connected concepts in the source material.
Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.