Merge pull request dennybritz#195 from pieromacaluso/master

Updated links to new version of Sutton and Barto's book

Author: dennybritz

DP/README.md

@@ -28,7 +28,7 @@
 
 **Optional:**
 
-- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/bookdraft2018jan1.pdf) - Chapter 4: Dynamic Programming
+- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/RLbook2018.pdf) - Chapter 4: Dynamic Programming
 
 
 ### Exercises

FA/README.md

@@ -25,8 +25,8 @@
 **Required:**
 
 - David Silver's RL Course Lecture 6 - Value Function Approximation ([video](https://www.youtube.com/watch?v=UoPei5o4fps), [slides](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/FA.pdf))
-- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/bookdraft2018jan1.pdf) - Chapter 9: On-policy Prediction with Approximation
-- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/bookdraft2018jan1.pdf) - Chapter 10: On-policy Control with Approximation
+- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/RLbook2018.pdf) - Chapter 9: On-policy Prediction with Approximation
+- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/RLbook2018.pdf) - Chapter 10: On-policy Control with Approximation
 
 **Optional:**
 

Introduction/README.md

@@ -17,7 +17,7 @@
 
 **Required:**
 
-- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/bookdraft2018jan1.pdf) - Chapter 1: The Reinforcement Learning Problem
+- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/RLbook2018.pdf) - Chapter 1: The Reinforcement Learning Problem
 - David Silver's RL Course Lecture 1 - Introduction to Reinforcement Learning ([video](https://www.youtube.com/watch?v=2pWv7GOvuf0), [slides](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/intro_RL.pdf))
 - [OpenAI Gym Tutorial](https://gym.openai.com/docs)
 

MC/README.md

@@ -26,7 +26,7 @@
 
 **Required:**
 
-- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/bookdraft2018jan1.pdf) - Chapter 5: Monte Carlo Methods
+- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/RLbook2018.pdf) - Chapter 5: Monte Carlo Methods
 
 
 **Optional:**

MDP/README.md

@@ -25,7 +25,7 @@
 
 **Required:**
 
-- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/bookdraft2018jan1.pdf) - Chapter 3: Finite Markov Decision Processes
+- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/RLbook2018.pdf) - Chapter 3: Finite Markov Decision Processes
 - David Silver's RL Course Lecture 2 - Markov Decision Processes ([video](https://www.youtube.com/watch?v=lfHX2hHRMVQ), [slides](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/MDP.pdf))
 
 

PolicyGradient/README.md

@@ -36,7 +36,7 @@
 
 **Optional:**
 
-- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/bookdraft2018jan1.pdf) - Chapter 13: Policy Gradient Methods
+- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/RLbook2018.pdf) - Chapter 13: Policy Gradient Methods
 - [Deterministic Policy Gradient Algorithms](http://jmlr.org/proceedings/papers/v32/silver14.pdf)
 - [Deterministic Policy Gradient Algorithms (Talk)](http://techtalks.tv/talks/deterministic-policy-gradient-algorithms/61098/)
 - [Continuous control with deep reinforcement learning](https://arxiv.org/abs/1509.02971)

README.md

@@ -2,7 +2,7 @@
 
 This repository provides code, exercises and solutions for popular Reinforcement Learning algorithms. These are meant to serve as a learning tool to complement the theoretical materials from
 
-- [Reinforcement Learning: An Introduction (2nd Edition)](http://incompleteideas.net/book/bookdraft2018jan1.pdf)
+- [Reinforcement Learning: An Introduction (2nd Edition)](http://incompleteideas.net/book/RLbook2018.pdf)
 - [David Silver's Reinforcement Learning Course](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html)
 
 Each folder in corresponds to one or more chapters of the above textbook and/or course. In addition to exercises and solution, each folder also contains a list of learning goals, a brief concept summary, and links to the relevant readings.
@@ -50,7 +50,7 @@ All code is written in Python 3 and uses RL environments from [OpenAI Gym](https
 
 Textbooks:
 
-- [Reinforcement Learning: An Introduction (2nd Edition)](http://incompleteideas.net/book/bookdraft2018jan1.pdf)
+- [Reinforcement Learning: An Introduction (2nd Edition)](http://incompleteideas.net/book/RLbook2018.pdf)
 
 Classes:
 

TD/README.md

@@ -28,14 +28,14 @@
 
 **Required:**
 
-- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/bookdraft2018jan1.pdf) - Chapter 6: Temporal-Difference Learning
+- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/RLbook2018.pdf) - Chapter 6: Temporal-Difference Learning
 - David Silver's RL Course Lecture 4 - Model-Free Prediction ([video](https://www.youtube.com/watch?v=PnHCvfgC_ZA), [slides](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/MC-TD.pdf))
 - David Silver's RL Course Lecture 5 - Model-Free Control ([video](https://www.youtube.com/watch?v=0g4j2k_Ggc4), [slides](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/control.pdf))
 
 **Optional:**
 
-- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/bookdraft2018jan1.pdf) - Chapter 7: Multi-Step Bootstrapping
-- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/bookdraft2018jan1.pdf) - Chapter 12: Eligibility Traces
+- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/RLbook2018.pdf) - Chapter 7: Multi-Step Bootstrapping
+- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/RLbook2018.pdf) - Chapter 12: Eligibility Traces
 
 
 ### Exercises