Difference between revisions of "Evolutionary Computation / Genetic Algorithms"
| Line 8: | Line 8: | ||
[http://www.google.com/search?q=evolution+genetic+algorithm+machine+learning+ML ...Google search] | [http://www.google.com/search?q=evolution+genetic+algorithm+machine+learning+ML ...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)]] | ||
| + | ** [[Deep Q Network (DQN)]] | ||
| + | ** Evolutionary Computation / Genetic Algorithms | ||
| + | ** [[Actor Critic]] | ||
| + | *** [[Advanced Actor Critic (A2C)]] | ||
| + | *** [[Asynchronous Advantage Actor Critic (A3C)]] | ||
| + | *** [[Lifelong Latent Actor-Critic (LILAC)]] | ||
| + | ** [[Hierarchical Reinforcement Learning (HRL)]] | ||
* [[Architectures]] | * [[Architectures]] | ||
* [[Python#TPOT|TPOT]] - automates the building of ML [[pipeline]]s by combining a flexible expression tree representation of [[pipeline]]s with stochastic search algorithms such as genetic programming. | * [[Python#TPOT|TPOT]] - automates the building of ML [[pipeline]]s by combining a flexible expression tree representation of [[pipeline]]s with stochastic search algorithms such as genetic programming. | ||
Revision as of 11:50, 3 July 2020
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)
- Deep Q Network (DQN)
- Evolutionary Computation / Genetic Algorithms
- Actor Critic
- Hierarchical Reinforcement Learning (HRL)
- Architectures
- TPOT - automates the building of ML pipelines by combining a flexible expression tree representation of pipelines with stochastic search algorithms such as genetic programming.
- Neural Architecture Search (NAS) with Evolution | Wikipedia
- ACM Special Interest Group on Genetic and Evolutionary Computation (SIGEVO)
- Publication - Evolving Artificial Intelligence Laboratory | University of Wyoming
- NeuroEvolution of Augmenting Topologies (NEAT)
- Topology and Weight Evolving Artificial Neural Network (TWEANN)
- 2017: The Year of Neuroevolution | Grigory Sapunov
- Other Challenges in Artificial Intelligence
- Reinforcement Learning (RL)
- A Beginner's Guide to Genetic & Evolutionary Algorithms | Chris Nicholson - A.I. Wiki pathmind
- Feature Engineering and Selection: A Practical Approach for Predictive Models -12.3 Genetic Algorithms | Max Kuhn and Kjell Johnson
Nature
Evolution of Mind
