CMU 10703: Deep Reinforcement Learning and Control, Spring 2017

Homepage

Warm up

• First Chapters from Reinforcement Learning: an Introduction, Sutton&Barto ,Second Edition (pdf) & also ebook here
• Dave Silver’s course and lecture videos on reinforcement learning

Schedule

 

Date	Topics	Lecturer	Readings	Additional Material
Wed Jan 18	Course Introduction	Katerina
Mon Jan 23	Intro to MDPs, POMDPs	Katerina	Sutton & Barto Ch 3
Wed Jan 25	Solving known MDPs: Dynamic Programming, Value Iteration, Policy Iteration, Policy Evaluation	Katerina	Sutton & Barto Ch 4
Mon Jan 30	Monte Carlo Learning: value function estimation and optimization	Russ	Sutton & Barto Ch 5
Wed Feb 1	Temporal Difference Learning: value function estimation and optimization, Q learning, SARSA	Russ	Sutton & Barto Ch 6
Mon Feb 6	Planning and Learning(1): Tabular methods, Dyna, Monte Carlo Tree Search	Katerina	Sutton & Barto Ch 8	A Survey of Monte Carlo Tree Search Methods http://www.cameronius.com/cv/mcts-survey-master.pdf
Wed Feb 8	Value function approximation, Deep Learning, Convnets, backpropagation	Russ
Mon Feb 13	Value function approximation, Deep Learning, Convnets, backpropagation	Russ
Wed Feb 15	Deep Q Learning : Double Q learning, replay memory	Russ
Mon Feb 20	Policy Gradients (1): REINFORCE, Natural Policy gradients,Variance reduction in gradient estimation, Actor-Critic, Deep Actor-Critic, TRPO	Russ	Sutton & Barto Ch 13
Wed Feb 22	Policy Gradients (2)	Russ
Mon Feb 27	Policy Gradients (3)	Russ
Wed Mar 1	Closer look at Continuous Actions, Variational Autoencoders, multimodal stochastic policies	Russ
Mon Mar 6	Exploration(1)	Katerina		Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models https://arxiv.org/abs/1507.00814, Variational Information Maximizing Exploration https://arxiv.org/abs/1605.09674, visitation counts, hashing
Wed Mar 8	Imitation learning(1): mimicking experts, behaviour cloning	Katerina		An Invitation to Imitation http://www.ri.cmu.edu/publication_view.html?pub_id=7891 Generative adversarial imitation learning https://arxiv.org/abs/1606.03476
Mon Mar 13	Spring break!
Wed Mar 15	Spring break!
Mon Mar 20	Imitation learning(2): Learning reward functions from demonstration, IOC, IRL			A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning http://www.jmlr.org/proceedings/papers/v15/ross11a/ross11a.pdf, Generative adversarial imitation learning https://arxiv.org/abs/1606.03476, Maximum entropy inverse reinforcement learning http://www.aaai.org/Papers/AAAI/2008/AAAI08-227.pdf,Learning to search: Functional gradient techniques for imitation learning http://www.ri.cmu.edu/publication_view.html?pub_id=6410
Wed Mar 22	Intro to optimal control, Differential Dynamic Programming, LQR, iterative-LQR	Katerina
Mon Mar 27	Imitation learning(3): learning from optimal controllers, self trials	Katerina		End-to-End Training of Deep Visuomotor Policies https://arxiv.org/pdf/1504.00702.pdf, PLATO: Policy Learning using Adaptive Trajectory Optimization, https://arxiv.org/pdf/1603.00622v3.pdf
Wed Mar 29	Planning and Learning(2): Learning Forward/Backward Models from experience, Planning with learned forward models, simulation to real world adaptation	Katerina		SE3-Nets: Learning Rigid Body Motion using Deep Neural Networks https://arxiv.org/pdf/1606.02378v2.pdf
Mon Apr 3	Planning and Learning(3)
4	Case studies: Alpha Go, deep math	Katerina
Mon Apr 10	Modular / Hierarchical RL (1): compositionality, temporal abstraction
Wed Apr 12	Modular / Hierarchical RL (2): Multi-task learning, curriculum learning	Russ
Mon Apr 17	Exploration(2):Learning and exploration in 3D environments, Long Term Memory	Russ
Wed Apr 19	Learning Motor Control: inspiration from Psychology		Sutton & Barto Ch 14,15
Mon Apr 24	Frontiers/Open Problems	Katerina
Wed Apr 26	Project Presentations
Mon May 1	Project Presentations
Wed May 3	Project Presentations

Log

Week 1:

Jan 18 - Introduction

• Slide 1
  • 1/23/2017;
• First Chapters from Reinforcement Learning: an Introduction, Sutton&Barto ,Second Edition
  • Chapter 1: 1/19/2017;
• Lecture 1 & 2 from Dave Silver’s course and lecture videos on reinforcement learning
  • Lecture 1: 1/17/2017;

Week 2:

Jan 23 - Intro to MDPs, POMDPs

• Slide
• Sutton & Barto Ch 3
  • 3.1, 3.2, 3.3: 1/23/2017;

Jan 25 - Solving known MDPs: Dynamic Programming, Value Iteration, Policy Iteration, Policy Evaluation

• Slide
• Sutton & Barto Ch 4
  • 4.1: 1/25/2017;
• implement Markov Decision Processes in Python

  • AIMA Python file: mdp.py (code taken from Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig)

 

转载于:https://www.cnblogs.com/casperwin/p/6295396.html