Difference between revisions of "Distributed Deep Reinforcement Learning (DDRL)"
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* [http://github.com/deepmind/lab/tree/master/game_scripts/levels/contributed/dmlab30 DMLab-30 | GitHub] | * [http://github.com/deepmind/lab/tree/master/game_scripts/levels/contributed/dmlab30 DMLab-30 | GitHub] | ||
| + | * [http://www.techleer.com/articles/488-impala-distributed-agent-in-dmlab-30/ IMPALA distributed agent in DMLab-30] | ||
| + | * [http://www.semanticscholar.org/paper/IMPALA%3A-Scalable-Distributed-Deep-RL-with-Weighted-Espeholt-Soyer/cbddc41e5b07ad8e3f7982e232bafba84c8419cc IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures] | ||
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| + | http://s3.ap-south-1.amazonaws.com/techleerimages/4d62b60c-4dcd-4774-9c75-417eba1cbbc1.png | ||
Revision as of 21:46, 15 February 2019
Youtube search... ...Google search
- 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
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
