Difference between revisions of "Distributed Deep Reinforcement Learning (DDRL)"
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* [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 | + | * [[Decentralized: Federated & Distributed]] Learning |
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* [[Reinforcement Learning (RL)]] | * [[Reinforcement Learning (RL)]] | ||
** [[Monte Carlo]] (MC) Method - Model Free Reinforcement Learning | ** [[Monte Carlo]] (MC) Method - Model Free Reinforcement Learning | ||
Revision as of 10:52, 26 September 2020
Youtube search... ...Google search
- Importance Weighted Actor-Learner Architectures: Scalable Distributed DeepRL in DMLab-30
- Decentralized: Federated & Distributed 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.
