Difference between revisions of "Deep Q Network (DQN)"
| Line 18: | Line 18: | ||
** [[Evolutionary Computation / Genetic Algorithms]] | ** [[Evolutionary Computation / Genetic Algorithms]] | ||
** [[Actor Critic]] | ** [[Actor Critic]] | ||
| + | *** [[Asynchronous Advantage Actor Critic (A3C)]] | ||
*** [[Advanced Actor Critic (A2C)]] | *** [[Advanced Actor Critic (A2C)]] | ||
| − | |||
*** [[Lifelong Latent Actor-Critic (LILAC)]] | *** [[Lifelong Latent Actor-Critic (LILAC)]] | ||
** [[Hierarchical Reinforcement Learning (HRL)]] | ** [[Hierarchical Reinforcement Learning (HRL)]] | ||
| + | |||
| + | |||
* [[Gaming]] | * [[Gaming]] | ||
Revision as of 06:20, 6 July 2020
Youtube search... ...Google search
- Reinforcement Learning (RL)
- Monte Carlo (MC) Method - Model Free Reinforcement Learning
- Markov Decision Process (MDP)
- State-Action-Reward-State-Action (SARSA)
- Q Learning
- Deep Q Network (DQN)
- Deep Reinforcement Learning (DRL) DeepRL
- Distributed Deep Reinforcement Learning (DDRL)
- Evolutionary Computation / Genetic Algorithms
- Actor Critic
- Hierarchical Reinforcement Learning (HRL)
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
