Difference between revisions of "Distributed Deep Reinforcement Learning (DDRL)"
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Revision as of 14:34, 1 September 2019
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- IMPALA (Importance Weighted Actor-Learner Architecture)
- Importance Weighted Actor-Learner Architectures: Scalable Distributed DeepRL in DMLab-30
- Deep Reinforcement Learning (DRL)
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
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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
