Difference between revisions of "Actor Critic"
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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 | ||
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** [[Distributed Deep Reinforcement Learning (DDRL)]] | ** [[Distributed Deep Reinforcement Learning (DDRL)]] | ||
** [[Deep Q Network (DQN)]] | ** [[Deep Q Network (DQN)]] | ||
Revision as of 16:45, 1 September 2019
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- 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
- Hierarchical Reinforcement Learning (HRL)
- MERLIN
- 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.
Asynchronous Advantage Actor Critic (A3C)
