State-of-the-art Research of Deep Reinforcement-learning

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
Requirements
An interset on deep reinforcement-learning research
Description
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
Who this course is for
Anyone who interset on deep reinforcement-learning research

Homepage

https://www.udemy.com/course/state-of-the-art-research-of-deep-reinforcement-learning/