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

OpenAI research, DeepMind research, Google research, Microsoft research

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