Merge pull request dennybritz#120 from jonahweissman/master

Fix links in all the `README.md`s

Author: dennybritz

DP/README.md

@@ -34,13 +34,13 @@
 ### Exercises
 
 - Implement Policy Evaluation in Python (Gridworld)
-  - [Exercise](Policy Evaluation.ipynb)
-  - [Solution](Policy Evaluation Solution.ipynb)
+  - [Exercise](Policy%20Evaluation.ipynb)
+  - [Solution](Policy%20Evaluation%20Solution.ipynb)
 
 - Implement Policy Iteration in Python (Gridworld)
-  - [Exercise](Policy Iteration.ipynb)
-  - [Solution](Policy Iteration Solution.ipynb)
+  - [Exercise](Policy%20Iteration.ipynb)
+  - [Solution](Policy%20Iteration%20Solution.ipynb)
 
 - Implement Value Iteration in Python (Gridworld)
-  - [Exercise](Value Iteration.ipynb)
-  - [Solution](Value Iteration Solution.ipynb)
+  - [Exercise](Value%20Iteration.ipynb)
+  - [Solution](Value%20Iteration%20Solution.ipynb)

DQN/README.md

@@ -39,11 +39,11 @@
 
 ### Exercises
 
-- [OpenAI Gym Atari Environment Playground](Breakout Playground.ipynb)
+- [OpenAI Gym Atari Environment Playground](Breakout%20Playground.ipynb)
 - Deep-Q Learning for Atari Games
-  - [Exercise](Deep Q Learning.ipynb)
-  - [Solution](Deep Q Learning Solution.ipynb)
+  - [Exercise](Deep%20Q%20Learning.ipynb)
+  - [Solution](Deep%20Q%20Learning%20Solution.ipynb)
 - Double-Q Learning
   - This is a minimal change to Q-Learning so use the same exercise as above
-  - [Solution](Double DQN Solution.ipynb)
+  - [Solution](Double%20DQN%20Solution.ipynb)
 - Prioritized Experience Replay (WIP)

FA/README.md

@@ -36,5 +36,5 @@
 ### Exercises
 
 - Solve Mountain Car Problem using Q-Learning with Linear Function Approximation
-  - [Exercise](Q-Learning with Value Function Approximation.ipynb)
-  - [Solution](Q-Learning with Value Function Approximation Solution.ipynb)
+  - [Exercise](Q-Learning%20with%20Value%20Function%20Approximation.ipynb)
+  - [Solution](Q-Learning%20with%20Value%20Function%20Approximation%20Solution.ipynb)

MC/README.md

@@ -37,13 +37,13 @@
 
 ### Exercises
 
-- [Get familiar with the Blackjack environment (Blackjack-v0)](Blackjack Playground.ipynb)
+- [Get familiar with the Blackjack environment (Blackjack-v0)](Blackjack%20Playground.ipynb)
 - Implement the Monte Carlo Prediction to estimate state-action values
-  - [Exercise](MC Prediction.ipynb)
-  - [Solution](MC Prediction Solution.ipynb)
+  - [Exercise](MC%20Prediction.ipynb)
+  - [Solution](MC%20Prediction%20Solution.ipynb)
 - Implement the on-policy first-visit Monte Carlo Control algorithm
-  - [Exercise](MC Control with Epsilon-Greedy Policies.ipynb)
-  - [Solution](MC Control with Epsilon-Greedy Policies Solution.ipynb)
+  - [Exercise](MC%20Control%20with%20Epsilon-Greedy%20Policies.ipynb)
+  - [Solution](MC%20Control%20with%20Epsilon-Greedy%20Policies%20Solution.ipynb)
 - Implement the off-policy every-visit Monte Carlo Control using Weighted Important Sampling algorithm
-  - [Exercise](Off-Policy MC Control with Weighted Importance Sampling.ipynb)
-  - [Solution](Off-Policy MC Control with Weighted Importance Sampling Solution.ipynb)
+  - [Exercise](Off-Policy%20MC%20Control%20with%20Weighted%20Importance%20Sampling.ipynb)
+  - [Solution](Off-Policy%20MC%20Control%20with%20Weighted%20Importance%20Sampling%20Solution.ipynb)

PolicyGradient/README.md

@@ -50,13 +50,13 @@
 
 - REINFORCE with Baseline
   - Exercise
-  - [Solution](CliffWalk REINFORCE with Baseline Solution.ipynb)
+  - [Solution](CliffWalk%20REINFORCE%20with%20Baseline%20Solution.ipynb)
 - Actor-Critic with Baseline
   - Exercise
-  - [Solution](CliffWalk Actor-Critic Solution.ipynb)
+  - [Solution](CliffWalk%20Actor-Critic%20Solution.ipynb)
 - Actor-Critic with Baseline for Continuous Action Spaces
   - Exercise
-  - [Solution](Continuous MountainCar Actor-Critic Solution.ipynb)
+  - [Solution](Continuous%20MountainCar%20Actor-Critic%20Solution.ipynb)
 - Deterministic Policy Gradients for Continuous Action Spaces (WIP)
 - Deep Deterministic Policy Gradients (WIP)
 - Asynchronous Advantage Actor-Critic (A3C)

README.md

@@ -26,21 +26,21 @@ All code is written in Python 3 and uses RL environments from [OpenAI Gym](https
 
 ### List of Implemented Algorithms
 
-- [Dynamic Programming Policy Evaluation](DP/Policy Evaluation Solution.ipynb)
-- [Dynamic Programming Policy Iteration](DP/Policy Iteration Solution.ipynb)
-- [Dynamic Programming Value Iteration](DP/Value Iteration Solution.ipynb)
-- [Monte Carlo Prediction](MC/MC Prediction Solution.ipynb)
-- [Monte Carlo Control with Epsilon-Greedy Policies](MC/MC Control with Epsilon-Greedy Policies Solution.ipynb)
-- [Monte Carlo Off-Policy Control with Importance Sampling](MC/Off-Policy MC Control with Weighted Importance Sampling Solution.ipynb)
-- [SARSA (On Policy TD Learning)](TD/SARSA Solution.ipynb)
-- [Q-Learning (Off Policy TD Learning)](TD/Q-Learning Solution.ipynb)
-- [Q-Learning with Linear Function Approximation](FA/Q-Learning with Value Function Approximation Solution.ipynb)
-- [Deep Q-Learning for Atari Games](DQN/Deep Q Learning Solution.ipynb)
-- [Double Deep-Q Learning for Atari Games](DQN/Double DQN Solution.ipynb)
+- [Dynamic Programming Policy Evaluation](DP/Policy%20Evaluation%20Solution.ipynb)
+- [Dynamic Programming Policy Iteration](DP/Policy%20Iteration%20Solution.ipynb)
+- [Dynamic Programming Value Iteration](DP/Value%20Iteration%20Solution.ipynb)
+- [Monte Carlo Prediction](MC/MC%20Prediction%20Solution.ipynb)
+- [Monte Carlo Control with Epsilon-Greedy Policies](MC/MC%20Control%20with%20Epsilon-Greedy%20Policies%20Solution.ipynb)
+- [Monte Carlo Off-Policy Control with Importance Sampling](MC/Off-Policy%20MC%20Control%20with%20Weighted%20Importance%20Sampling%20Solution.ipynb)
+- [SARSA (On Policy TD Learning)](TD/SARSA%20Solution.ipynb)
+- [Q-Learning (Off Policy TD Learning)](TD/Q-Learning%20Solution.ipynb)
+- [Q-Learning with Linear Function Approximation](FA/Q-Learning%20with%20Value%20Function%20Approximation%20Solution.ipynb)
+- [Deep Q-Learning for Atari Games](DQN/Deep%20Q%20Learning%20Solution.ipynb)
+- [Double Deep-Q Learning for Atari Games](DQN/Double%20DQN%20Solution.ipynb)
 - Deep Q-Learning with Prioritized Experience Replay (WIP)
-- [Policy Gradient: REINFORCE with Baseline](PolicyGradient/CliffWalk REINFORCE with Baseline Solution.ipynb)
-- [Policy Gradient: Actor Critic with Baseline](PolicyGradient/CliffWalk Actor Critic Solution.ipynb)
-- [Policy Gradient: Actor Critic with Baseline for Continuous Action Spaces](PolicyGradient/Continuous MountainCar Actor Critic Solution.ipynb)
+- [Policy Gradient: REINFORCE with Baseline](PolicyGradient/CliffWalk%20REINFORCE%20with%20Baseline%20Solution.ipynb)
+- [Policy Gradient: Actor Critic with Baseline](PolicyGradient/CliffWalk%20Actor%20Critic%20Solution.ipynb)
+- [Policy Gradient: Actor Critic with Baseline for Continuous Action Spaces](PolicyGradient/Continuous%20MountainCar%20Actor%20Critic%20Solution.ipynb)
 - Deterministic Policy Gradients for Continuous Action Spaces (WIP)
 - Deep Deterministic Policy Gradients (DDPG) (WIP)
 - [Asynchronous Advantage Actor Critic (A3C)](PolicyGradient/a3c)

TD/README.md

@@ -40,11 +40,11 @@
 
 ### Exercises
 
-- [Windy Gridworld Playground](Windy Gridworld Playground.ipynb)
+- [Windy Gridworld Playground](Windy%20Gridworld%20Playground.ipynb)
 - Implement SARSA
   - [Exercise](SARSA.ipynb)
-  - [Solution](SARSA Solution.ipynb)
-- [Cliff Environment Playground](Cliff Environment Playground.ipynb)
+  - [Solution](SARSA%20Solution.ipynb)
+- [Cliff Environment Playground](Cliff%20Environment%20Playground.ipynb)
 - Implement Q-Learning in Python
   - [Exercise](Q-Learning.ipynb)
-  - [Solution](Q-Learning Solution.ipynb)
+  - [Solution](Q-Learning%20Solution.ipynb)