Distributed Deep Reinforcement Learning (DDRL)

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• 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
  • Actor Critic
  • 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

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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