OpenAI research, DeepMind research, Google research, Microsoft research
Hello I am Nitsan Soffair, a Deep RL researcher at BGU.
What you’ll learn
- Get state-of-the-art knowledge of deep reinforcement-learning research.
- Be able to start deep reinforcement-learning research.
- Be able to get engineering job on deep reinforcement-learning.
- Be able to get research job on deep reinforcement-learning.
Course Content
- OpenAI research –> 6 lectures • 8min.
- DeepMind research –> 5 lectures • 6min.
- Google research –> 8 lectures • 13min.
- Microsoft research –> 1 lecture • 2min.
Requirements
Hello I am Nitsan Soffair, a Deep RL researcher at BGU.
In my State-of-the-art Research of Deep Reinforcement-learning course you will get the newest state-of-the-art Deep reinforcement-learning research knowledge.
You will do the following
- Get state-of-the-art research knowledge regarding
- OpenAI research
- DeepMind research
- Google research
- Microsoft research
- Validate your knowledge by answering short quizzes of each lecture.
- Be able to complete the course by ~2 hours.
Topics
- Advanced exploration methods
- Chatbot based Deep RL
- Evaluation strategies
- Advanced RL metrics
- Navigating robot get human language instructions
- Merging on-policy and off-policy gradient estimation
- Hierarchical RL
- More advanced topics
Syllabus
- OpenAI research
- Emergent Tool Use from Multi-Agent Interaction
- Learning Dexterity
- Emergent Complexity via Multi-Agent Competition
- Competitive Self-Play Better Exploration with Parameter Noise
- Proximal Policy Optimization
- Evolution Strategies as a Scalable Alternative to Reinforcement Learning
- DeepMind research
- Recurrent Experience Reply in distributed Reinforcement-learning
- Maximum a Posteriori Policy Optimization
- NeuPL: Neural Population Learning
- Learning more skills through optimistic exploration
- When should agents explore?
- Google brain research
- QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
- FollowNet: Robot Navigation by Following Natural Language Directions with Deep Reinforcement Learning
- Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning
- Scalable Deep Reinforcement Learning Algorithms for Mean Field
- Value-Based Deep Reinforcement Learning Requires Explicit Regularisation
- Air Learning: a deep reinforcement learning gym for autonomous aerial robot visual navigation
- Deep Reinforcement Learning at the Edge of the Statistical Precipice
- Exploration in Reinforcement Learning with Deep Covering Options
- Microsoft research
- Deep Reinforcement-learning for Dialogue Generation
Resources
- OpenAI papers
- DeepMind papers
- Google papers
- Microsoft papers
Get Tutorial
https://www.udemy.com/course/state-of-the-art-research-of-deep-reinforcement-learning/70e5931ee620ba35b88b14592546c86b08e562f1