Difference between revisions of "Deep Q Network (DQN)"
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* [[Reinforcement Learning (RL)]] | * [[Reinforcement Learning (RL)]] | ||
* [http://medium.com/deep-math-machine-learning-ai/ch-12-1-model-free-reinforcement-learning-algorithms-monte-carlo-sarsa-q-learning-65267cb8d1b4 Model Free Reinforcement learning algorithms (Monte Carlo, SARSA, Q-learning) | Madhu Sanjeevi (Mady) - Medium] | * [http://medium.com/deep-math-machine-learning-ai/ch-12-1-model-free-reinforcement-learning-algorithms-monte-carlo-sarsa-q-learning-65267cb8d1b4 Model Free Reinforcement learning algorithms (Monte Carlo, SARSA, Q-learning) | Madhu Sanjeevi (Mady) - Medium] | ||
| + | * [[Monte Carlo]] | ||
* [[Gaming]] | * [[Gaming]] | ||
* [http://en.wikipedia.org/wiki/Q-learning Q Learning | Wikipedia] | * [http://en.wikipedia.org/wiki/Q-learning Q Learning | Wikipedia] | ||
Revision as of 15:50, 11 August 2019
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- Deep Reinforcement Learning (DRL)
- Reinforcement Learning (RL)
- Model Free Reinforcement learning algorithms (Monte Carlo, SARSA, Q-learning) | Madhu Sanjeevi (Mady) - Medium
- Monte Carlo
- Gaming
- Q Learning | Wikipedia
When feedback is provided, it might be long time after the fateful decision has been made. In reality, the feedback is likely to be the result of a large number of prior decisions, taken amid a shifting, uncertain environment. Unlike supervised learning, there are no correct input/output pairs, so suboptimal actions are not explicitly corrected, wrong actions just decrease the corresponding value in the Q-table, meaning there’s less chance choosing the same action should the same state be encountered again. Quora | Jaron Collis
Training deep neural networks to show that a novel end-to-end reinforcement learning agent, termed a deep Q-network (DQN) Human-level control through Deep Reinforcement Learning | Deepmind
