Difference between revisions of "Distributed Deep Reinforcement Learning (DDRL)"
| Line 10: | Line 10: | ||
* [http://deepmind.com/blog/impala-scalable-distributed-deeprl-dmlab-30/ Importance Weighted Actor-Learner Architectures: Scalable Distributed DeepRL in DMLab-30] | * [http://deepmind.com/blog/impala-scalable-distributed-deeprl-dmlab-30/ Importance Weighted Actor-Learner Architectures: Scalable Distributed DeepRL in DMLab-30] | ||
* [[Federated]] Learning | * [[Federated]] Learning | ||
| − | * Reinforcement Learning (RL) | + | * [[Reinforcement Learning (RL)]] |
** [[Monte Carlo]] (MC) Method - Model Free Reinforcement Learning | ** [[Monte Carlo]] (MC) Method - Model Free Reinforcement Learning | ||
** [[Markov Decision Process (MDP)]] | ** [[Markov Decision Process (MDP)]] | ||
| + | ** [[State-Action-Reward-State-Action (SARSA)]] | ||
** [[Q Learning]] | ** [[Q Learning]] | ||
| − | ** [[ | + | *** [[Deep Q Network (DQN)]] |
| − | ** | + | ** Deep Reinforcement Learning (DRL) DeepRL |
| − | ** [[Deep | + | ** [[Distributed Deep Reinforcement Learning (DDRL)]] |
** [[Evolutionary Computation / Genetic Algorithms]] | ** [[Evolutionary Computation / Genetic Algorithms]] | ||
** [[Actor Critic]] | ** [[Actor Critic]] | ||
| + | *** [[Advanced Actor Critic (A2C)]] | ||
| + | *** [[Asynchronous Advantage Actor Critic (A3C)]] | ||
| + | *** [[Lifelong Latent Actor-Critic (LILAC)]] | ||
** [[Hierarchical Reinforcement Learning (HRL)]] | ** [[Hierarchical Reinforcement Learning (HRL)]] | ||
Revision as of 06:09, 6 July 2020
Youtube search... ...Google search
- Importance Weighted Actor-Learner Architectures: Scalable Distributed DeepRL in DMLab-30
- Federated Learning
- 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)
a new, highly scalable agent architecture for distributed training called Importance Weighted Actor-Learner Architecture that uses a new off-policy correction algorithm called V-trace.
