Difference between revisions of "Distributed Deep Reinforcement Learning (DDRL)"
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* [[IMPALA (Importance Weighted Actor-Learner Architecture)]] | * [[IMPALA (Importance Weighted Actor-Learner Architecture)]] | ||
* [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] | ||
| − | * [[Deep Reinforcement Learning (DRL)]] | + | * Reinforcement Learning (RL): |
| + | ** [[Monte Carlo]] (MC) Method - Model Free Reinforcement Learning | ||
| + | ** [[Markov Decision Process (MDP)]] | ||
| + | ** [[Q Learning]] | ||
| + | ** [[State-Action-Reward-State-Action (SARSA)]] | ||
| + | ** [[Deep Reinforcement Learning (DRL)]] DeepRL | ||
| + | *** [[IMPALA (Importance Weighted Actor-Learner Architecture)]] | ||
| + | ** [[Deep Q Network (DQN)]] | ||
| + | ** [[Evolutionary Computation / Genetic Algorithms]] | ||
| + | ** [[Asynchronous Advantage Actor Critic (A3C)]] | ||
| + | ** [[Hierarchical Reinforcement Learning (HRL)]] | ||
| + | *** [[HIerarchical Reinforcement learning with Off-policy correction(HIRO)]] | ||
| + | ** [[MERLIN]] | ||
| + | |||
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 agent architecture for distributed training called Importance Weighted Actor-Learner Architecture that uses a new off-policy correction algorithm called V-trace. | ||
Revision as of 15:13, 1 September 2019
Youtube search... ...Google search
- IMPALA (Importance Weighted Actor-Learner Architecture)
- Importance Weighted Actor-Learner Architectures: Scalable Distributed DeepRL in DMLab-30
- Reinforcement Learning (RL):
- Monte Carlo (MC) Method - Model Free Reinforcement Learning
- Markov Decision Process (MDP)
- Q Learning
- State-Action-Reward-State-Action (SARSA)
- Deep Reinforcement Learning (DRL) DeepRL
- Deep Q Network (DQN)
- Evolutionary Computation / Genetic Algorithms
- Asynchronous Advantage Actor Critic (A3C)
- Hierarchical Reinforcement Learning (HRL)
- MERLIN
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.
DMLab-30
Youtube search... ...Google search
DMLab-30 is a collection of new levels designed using our open source RL environment DeepMind Lab. These environments enable any DeepRL researcher to test systems on a large spectrum of interesting tasks either individually or in a multi-task setting.
- DMLab-30 | GitHub
- IMPALA distributed agent in DMLab-30
- IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
