updated the paper links in the docs and restyled the theme

docs/docs/algorithms/other/dfp.md

@@ -1,6 +1,8 @@
-> Actions space: Discrete
+# Direct Future Prediction
 
-[Paper](https://arxiv.org/abs/1611.01779)
+**Actions space:** Discrete
+
+**References:** [Learning to Act by Predicting the Future](https://arxiv.org/abs/1611.01779)
 
 ## Network Structure
 

docs/docs/algorithms/policy_optimization/ac.md

@@ -1,6 +1,8 @@
-> Action space: Discrete|Continuous
+# Actor-Critic
 
-[Paper](https://arxiv.org/abs/1602.01783)
+**Actions space:** Discrete|Continuous
+
+**References:** [Asynchronous Methods for Deep Reinforcement Learning](https://arxiv.org/abs/1602.01783)
 
 ## Network Structure 
 <p style="text-align: center;">

docs/docs/algorithms/policy_optimization/cppo.md

@@ -1,6 +1,8 @@
-> Action Space: Discrete|Continuous
+# Clipped Proximal Policy Optimization
 
-[Paper](https://arxiv.org/pdf/1707.06347.pdf)
+**Actions space:** Discrete|Continuous
+
+**References:** [Proximal Policy Optimization Algorithms](https://arxiv.org/pdf/1707.06347.pdf)
 
 ## Network Structure
 

docs/docs/algorithms/policy_optimization/ddpg.md

@@ -1,6 +1,8 @@
-> Actions space: Continuous
+# Deep Deterministic Policy Gradient
 
-[Paper](https://arxiv.org/abs/1509.02971)
+**Actions space:** Continuous
+
+**References:** [Continuous control with deep reinforcement learning](https://arxiv.org/abs/1509.02971)
 
 ## Network Structure
 

docs/docs/algorithms/policy_optimization/pg.md

@@ -1,6 +1,8 @@
-> Action Space: Discrete|Continuous
+# Policy Gradient
 
-[Paper](http://www-anw.cs.umass.edu/~barto/courses/cs687/williams92simple.pdf)
+**Actions space:** Discrete|Continuous
+
+**References:** [Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning](http://www-anw.cs.umass.edu/~barto/courses/cs687/williams92simple.pdf)
 
 ## Network Structure
 

docs/docs/algorithms/policy_optimization/ppo.md

@@ -1,6 +1,8 @@
-> Actions space: Discrete|Continuous
+# Proximal Policy Optimization
 
-[Paper](https://arxiv.org/pdf/1707.02286.pdf)
+**Actions space:** Discrete|Continuous
+
+**References:** [Emergence of Locomotion Behaviours in Rich Environments](https://arxiv.org/pdf/1707.02286.pdf)
 
 ## Network Structure
 

docs/docs/algorithms/value_optimization/bs_dqn.md

@@ -1,6 +1,8 @@
-> Action space: Discrete
+# Bootstrapped DQN
 
-[Paper](https://arxiv.org/abs/1602.04621)
+**Actions space:** Discrete
+
+**References:** [Deep Exploration via Bootstrapped DQN](https://arxiv.org/abs/1602.04621)
 
 ## Network Structure
 

docs/docs/algorithms/value_optimization/distributional_dqn.md

@@ -1,6 +1,8 @@
-> Action space: Discrete
+# Distributional DQN
 
-[Paper](https://arxiv.org/abs/1707.06887)
+**Actions space:** Discrete
+
+**References:** [A Distributional Perspective on Reinforcement Learning](https://arxiv.org/abs/1707.06887)
 
 ## Network Structure
 

docs/docs/algorithms/value_optimization/double_dqn.md

@@ -1,8 +1,8 @@
 # Double DQN
-> Action space: Discrete
 
- [Paper](https://arxiv.org/pdf/1509.06461.pdf) 
+**Actions space:** Discrete
 
+**References:** [Deep Reinforcement Learning with Double Q-learning](https://arxiv.org/abs/1509.06461.pdf)
 
 ## Network Structure
 

docs/docs/algorithms/value_optimization/dqn.md

@@ -1,6 +1,8 @@
-> Action space: Discrete
+# Deep Q Networks
 
-[Paper](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf)
+**Actions space:** Discrete
+
+**References:** [Playing Atari with Deep Reinforcement Learning](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf)
 
 ## Network Structure
 

docs/docs/algorithms/value_optimization/dueling_dqn.md

@@ -1,6 +1,8 @@
-> Action space: Discrete
+# Dueling DQN
 
-[Paper](https://arxiv.org/abs/1511.06581)
+**Actions space:** Discrete
+
+**References:** [Dueling Network Architectures for Deep Reinforcement Learning](https://arxiv.org/abs/1511.06581)
 
 ## Network Structure
 

docs/docs/algorithms/value_optimization/mmc.md

@@ -1,8 +1,8 @@
 # Mixed Monte Carlo
 
-> Action space: Discrete
+**Actions space:** Discrete
 
-[Paper](https://arxiv.org/abs/1703.01310)
+**References:** [Count-Based Exploration with Neural Density Models](https://arxiv.org/abs/1703.01310)
 
 ## Network Structure
 

docs/docs/algorithms/value_optimization/n_step.md

@@ -1,6 +1,8 @@
-> Action space: Discrete
+# N-Step Q Learning
 
-[Paper](https://arxiv.org/abs/1602.01783)
+**Actions space:** Discrete
+
+**References:** [Asynchronous Methods for Deep Reinforcement Learning](https://arxiv.org/abs/1602.01783)
 
 ## Network Structure
 

docs/docs/algorithms/value_optimization/naf.md

@@ -1,6 +1,8 @@
-> Action space: Continuous
+# Normalized Advantage Functions
 
-[Paper](https://arxiv.org/abs/1603.00748.pdf)
+**Actions space:** Continuous
+
+**References:** [Continuous Deep Q-Learning with Model-based Acceleration](https://arxiv.org/abs/1603.00748.pdf)
 
 ## Network Structure
 

docs/docs/algorithms/value_optimization/nec.md

@@ -1,6 +1,8 @@
-> Action space: Discrete
+# Neural Episodic Control
 
-[Paper](https://arxiv.org/abs/1703.01988)
+**Actions space:** Discrete
+
+**References:** [Neural Episodic Control](https://arxiv.org/abs/1703.01988)
 
 ## Network Structure
 

docs/docs/algorithms/value_optimization/pal.md

@@ -1,6 +1,8 @@
-> Action space: Discrete
+# Persistent Advantage Learning
 
-[Paper](https://arxiv.org/abs/1512.04860)
+**Actions space:** Discrete
+
+**References:** [Increasing the Action Gap: New Operators for Reinforcement Learning](https://arxiv.org/abs/1512.04860)
 
 ## Network Structure
 

docs/docs/extra.css

@@ -0,0 +1,3 @@
+.wy-side-nav-search {
+    background-color: #79a7a5;
+}

docs/mkdocs.yml

@@ -6,6 +6,7 @@ markdown_extensions:
     enable_dollar_delimiter: True #for use of inline $..$
 
 extra_javascript: ['https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS_HTML']
+extra_css: [extra.css]
 
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 - Home : index.md