Difference between revisions of "Deep Q Network (DQN)"
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** [[State-Action-Reward-State-Action (SARSA)]] | ** [[State-Action-Reward-State-Action (SARSA)]] | ||
** [[Deep Reinforcement Learning (DRL)]] DeepRL | ** [[Deep Reinforcement Learning (DRL)]] DeepRL | ||
| − | |||
** [[Distributed Deep Reinforcement Learning (DDRL)]] | ** [[Distributed Deep Reinforcement Learning (DDRL)]] | ||
** [[Evolutionary Computation / Genetic Algorithms]] | ** [[Evolutionary Computation / Genetic Algorithms]] | ||
| − | ** [[ | + | ** [[Actor Critic]] |
** [[Hierarchical Reinforcement Learning (HRL)]] | ** [[Hierarchical Reinforcement Learning (HRL)]] | ||
| − | |||
** [[MERLIN]] | ** [[MERLIN]] | ||
Revision as of 17:49, 1 September 2019
Youtube search... ...Google search
- Reinforcement Learning (RL):
- Monte Carlo (MC) Method - Model Free Reinforcement Learning
- Markov Decision Process (MDP)
- Q Learning
- State-Action-Reward-State-Action (SARSA)
- Deep Reinforcement Learning (DRL) DeepRL
- Distributed Deep Reinforcement Learning (DDRL)
- Evolutionary Computation / Genetic Algorithms
- Actor Critic
- Hierarchical Reinforcement Learning (HRL)
- MERLIN
Deep Q learning (DQN), as published in Playing Atari with Deep Reinforcement Learning | Mnih et al, 2013, leverages advances in deep learning to learn policies from high dimensional sensory input. A convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. Vanilla Deep Q Networks: Deep Q Learning Explained | Chris Yoon - Towards Data Science
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
