Difference between revisions of "Actor Critic"
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| − | [ | + | [https://www.youtube.com/results?search_query=Asynchronous+Advantage+Actor+Critic+Reinforcement+Machine+Learning YouTube search...] |
| − | [ | + | [https://www.google.com/search?q=Asynchronous+Advantage+Actor+Critic+Reinforcement+machine+learning+ML+artificial+intelligence ...Google search] |
* [[Reinforcement Learning (RL)]] | * [[Reinforcement Learning (RL)]] | ||
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| − | * [ | + | * [https://towardsdatascience.com/advanced-reinforcement-learning-6d769f529eb3 Beyond DQN/A3C: A Survey in Advanced Reinforcement Learning | Joyce Xu - Towards Data Science] |
* [[Policy Gradient (PG)]] | * [[Policy Gradient (PG)]] | ||
Revision as of 19:57, 27 March 2023
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 Reinforcement Learning (DRL) DeepRL
- Distributed Deep Reinforcement Learning (DDRL)
- Evolutionary Computation / Genetic Algorithms
- Actor Critic
- Hierarchical Reinforcement Learning (HRL)
- Beyond DQN/A3C: A Survey in Advanced Reinforcement Learning | Joyce Xu - Towards Data Science
- Policy Gradient (PG)
Policy gradients and Deep Q Network (DQN) can only get us so far, but what if we used two networks to help train and AI instead of one? Thats the idea behind actor critic algorithms.
