Difference between revisions of "Distributed Deep Reinforcement Learning (DDRL)"
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*** [[Lifelong Latent Actor-Critic (LILAC)]] | *** [[Lifelong Latent Actor-Critic (LILAC)]] | ||
** [[Hierarchical Reinforcement Learning (HRL)]] | ** [[Hierarchical Reinforcement Learning (HRL)]] | ||
| + | * [[Agents]] | ||
| − | 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. | + | a new, highly scalable [[Agents|agent]] architecture for distributed training called Importance Weighted Actor-Learner Architecture that uses a new off-policy correction algorithm called V-trace. |
<youtube>-YMfJLFynmA</youtube> | <youtube>-YMfJLFynmA</youtube> | ||
Revision as of 07:56, 4 February 2023
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)
- Agents
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.
