Difference between revisions of "Distributed Deep Reinforcement Learning (DDRL)"
| Line 16: | Line 16: | ||
** [[Q Learning]] | ** [[Q Learning]] | ||
*** [[Deep Q Network (DQN)]] | *** [[Deep Q Network (DQN)]] | ||
| − | ** Deep Reinforcement Learning (DRL) DeepRL | + | ** [[Deep Reinforcement Learning (DRL)]] DeepRL |
| − | ** | + | ** Distributed Deep Reinforcement Learning (DDRL) |
** [[Evolutionary Computation / Genetic Algorithms]] | ** [[Evolutionary Computation / Genetic Algorithms]] | ||
** [[Actor Critic]] | ** [[Actor Critic]] | ||
Revision as of 06:10, 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.
