Difference between revisions of "Distributed Deep Reinforcement Learning (DDRL)"
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|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | ||
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| − | [ | + | [https://www.youtube.com/results?search_query=Distributed+Deep+Reinforcement+Learning+DeepRL Youtube search...] |
| − | [ | + | [https://www.google.com/search?q=Distributed+Deep+Reinforcement+Learning+DeepRL+machine+learning+ML+artificial+intelligence ...Google search] |
| − | * [ | + | * [https://deepmind.com/blog/impala-scalable-distributed-deeprl-dmlab-30/ 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 Q Network (DQN)]] | ||
| + | ** [[Deep Reinforcement Learning (DRL)]] DeepRL | ||
| + | ** Distributed Deep Reinforcement Learning (DDRL) | ||
| + | ** [[Evolutionary Computation / Genetic Algorithms]] | ||
| + | ** [[Actor Critic]] | ||
| + | *** [[Asynchronous Advantage Actor Critic (A3C)]] | ||
| + | *** [[Advanced Actor Critic (A2C)]] | ||
| + | *** [[Lifelong Latent Actor-Critic (LILAC)]] | ||
| + | ** [[Hierarchical Reinforcement Learning (HRL)]] | ||
| + | * [[Agents]] ... [[Agents#Communication | communications]] | ||
| + | * [[Policy]] ... [[Policy vs Plan]] ... [[Constitutional AI]] ... [[Trust Region Policy Optimization (TRPO)]] ... [[Policy Gradient (PG)]] ... [[Proximal Policy Optimization (PPO)]] | ||
| − | |||
| − | <youtube> | + | |
| + | 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> | ||
Latest revision as of 15:36, 16 April 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 ... communications
- Policy ... Policy vs Plan ... Constitutional AI ... Trust Region Policy Optimization (TRPO) ... Policy Gradient (PG) ... Proximal Policy Optimization (PPO)
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.
