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

OpenAI research, DeepMind research, Google research, Microsoft research
Free tutorial
Rating: 1.0 out of 5 (1 rating)
594 students
30min of on-demand video
English
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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

  1. Get state-of-the-art research knowledge regarding

    1. OpenAI research

    2. DeepMind research

    3. Google research

    4. Microsoft research

  2. Validate your knowledge by answering short quizzes of each lecture.

  3. Be able to complete the course by ~2 hours.

Topics

  1. Advanced exploration methods

  2. Chatbot based Deep RL

  3. Evaluation strategies

  4. Advanced RL metrics

  5. Navigating robot get human language instructions

  6. Merging on-policy and off-policy gradient estimation

  7. Hierarchical RL

  8. More advanced topics

Syllabus

  1. OpenAI research

    1. Emergent Tool Use from Multi-Agent Interaction

    2. Learning Dexterity

    3. Emergent Complexity via Multi-Agent Competition

    4. Competitive Self-Play Better Exploration with Parameter Noise

    5. Proximal Policy Optimization

    6. Evolution Strategies as a Scalable Alternative to Reinforcement Learning

  2. DeepMind research

    1. Recurrent Experience Reply in distributed Reinforcement-learning

    2. Maximum a Posteriori Policy Optimization

    3. NeuPL: Neural Population Learning

    4. Learning more skills through optimistic exploration

    5. When should agents explore?

  3. Google brain research

    1. QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation

    2. FollowNet: Robot Navigation by Following Natural Language Directions with Deep Reinforcement Learning

    3. Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning

    4. Scalable Deep Reinforcement Learning Algorithms for Mean Field

    5. Value-Based Deep Reinforcement Learning Requires Explicit Regularisation

    6. Air Learning: a deep reinforcement learning gym for autonomous aerial robot visual navigation

    7. Deep Reinforcement Learning at the Edge of the Statistical Precipice

    8. Exploration in Reinforcement Learning with Deep Covering Options

  4. Microsoft research

    1. 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

Instructor

Deep RL researcher
Nitsan Soffair
  • 3.2 Instructor Rating
  • 21 Reviews
  • 5,239 Students
  • 5 Courses

Currently Deep RL researcher at BGU with Masters of CS at BGU.

My thesis topic is Single agent to multi agent (SA2MA) Deep MARL algorithm beats famoues WQMIX created by Shimon whiteson, Head of Waymo reasearch.

My main interest is AI, while I am very enthusiastic about the new research at NLP decided to start teaching as best way for learning.

I have 2 years experience of teaching-assistant at BGU, particularly in Reinforcement learning course.


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