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  01 - OpenAI research

   001 Emergent Tool Use from Multi-Agent Interaction_en.vtt

   002 Learning Dexterous In-Hand Manipulation_en.vtt

   003 Emergent Complexity via Multi-Agent Competition_en.vtt

   004 Parameter Space Noise for Exploration_en.vtt

   005 Proximal Policy Optimization Algorithms_en.vtt

   006 Evolution Strategies as a Scalable Alternative to Reinforcement Learning_en.vtt

  02 - DeepMind research

   001 NeuPL Neural Population Learning_en.vtt

   002 Learning more skills through optimistic exploration_en.vtt

   003 When should agents explore_en.vtt

   004 Recurrent Experience Reply in distributed Reinforcement-learning_en.vtt

   005 Maximum a Posteriori Policy Optimisation_en.vtt

  03 - Google research

   001 Scalable Deep Reinforcement Learning Algorithms for Mean Field Games_en.vtt

   002 Value-Based Deep Reinforcement Learning Requires Explicit Regularization_en.vtt

   003 Deep Reinforcement Learning at the Edge of the Statistical Precipice_en.vtt

   004 Exploration in Reinforcement Learning with Deep Covering Options_en.vtt

   005 Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation_en.vtt

   006 FollowNet Robot Navigation by Following Natural Language Directions with Deep R_en.vtt

   007 Interpolated Policy Gradient Merging On-Policy and Off-Policy Gradient Estimati_en.vtt

   008 Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning_en.vtt

  04 - Microsoft research

   001 Deep Reinforcement Learning for Dialogue Generation_en.vtt