Difference between revisions of "Advanced Actor Critic (A2C)"
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A2C produces comparable performance to [[Asynchronous Advantage Actor Critic (A3C)]] while being more efficient. A2C is like A3C but without the asynchronous part; this means a single-worker variant of the A3C. [http://towardsdatascience.com/understanding-actor-critic-methods-931b97b6df3f Understanding Actor Critic Methods and A2C | Chris Yoon - Towards Data Science] | A2C produces comparable performance to [[Asynchronous Advantage Actor Critic (A3C)]] while being more efficient. A2C is like A3C but without the asynchronous part; this means a single-worker variant of the A3C. [http://towardsdatascience.com/understanding-actor-critic-methods-931b97b6df3f Understanding Actor Critic Methods and A2C | Chris Yoon - Towards Data Science] | ||
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Revision as of 14:38, 3 July 2020
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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
- Actor Critic
- Advanced Actor Critic (A2C)
- Asynchronous Advantage Actor Critic (A3C)
- Lifelong Latent Actor-Critic (LILAC)
- Hierarchical Reinforcement Learning (HRL)
- Beyond DQN/A3C: A Survey in Advanced Reinforcement Learning | Joyce Xu - Towards Data Science
- Policy Gradient (PG)
A2C produces comparable performance to Asynchronous Advantage Actor Critic (A3C) while being more efficient. A2C is like A3C but without the asynchronous part; this means a single-worker variant of the A3C. Understanding Actor Critic Methods and A2C | Chris Yoon - Towards Data Science
