edit

README.md

@@ -1,6 +1,6 @@
 <p align="center">
     <a href="https://www.youtube.com/watch?v=pieI7rOXELI&list=PLXO45tsB95cIplu-fLMpUEEZTwrDNh6Ba" target="_blank">
-    <img width="60%" src="/blob/master/RL_cover.jpg" style="max-width:100%;">
+    <img width="60%" src="https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/blob/master/RL_cover.jpg" style="max-width:100%;">
     </a>
 </p>
 
@@ -18,67 +18,67 @@ In these tutorials for reinforcement learning, it covers from the basic RL algor
 # Table of Contents
 
 * Tutorials
-    * [Simple entry example](/tree/master/contents/1_command_line_reinforcement_learning)
-    * [Q-learning](/tree/master/contents/2_Q_Learning_maze)
-    * [Sarsa](/tree/master/contents/3_Sarsa_maze)
-    * [Sarsa(lambda)](/tree/master/contents/4_Sarsa_lambda_maze)
-    * [Deep Q Network](/tree/master/contents/5_Deep_Q_Network)
-    * [Using OpenAI Gym](/tree/master/contents/6_OpenAI_gym)
-    * [Double DQN](/tree/master/contents/5.1_Double_DQN)
-    * [DQN with Prioitized Experience Replay](/tree/master/contents/5.2_Prioritized_Replay_DQN)
-    * [Dueling DQN](/tree/master/contents/5.3_Dueling_DQN)
-    * [Policy Gradients](/tree/master/contents/7_Policy_gradient_softmax)
-    * [Actor Critic](/tree/master/contents/8_Actor_Critic_Advantage)
-    * [Deep Deterministic Policy Gradient](/tree/master/contents/9_Deep_Deterministic_Policy_Gradient_DDPG)
-    * [A3C](/tree/master/contents/10_A3C)
-    * [Dyna-Q](/tree/master/contents/11_Dyna_Q)
-    * [Proximal Policy Optimization (PPO)](/tree/master/contents/12_Proximal_Policy_Optimization)
-* [Some of my experiments](/tree/master/experiments)
-    * [2D Car](/tree/master/experiments/2D_car)
-    * [Robot arm](/tree/master/experiments/Robot_arm)
-    * [BipedalWalker](/tree/master/experiments/Solve_BipedalWalker)
-    * [LunarLander](/tree/master/experiments/Solve_LunarLander)
+    * [Simple entry example](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/1_command_line_reinforcement_learning)
+    * [Q-learning](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/2_Q_Learning_maze)
+    * [Sarsa](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/3_Sarsa_maze)
+    * [Sarsa(lambda)](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/4_Sarsa_lambda_maze)
+    * [Deep Q Network](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/5_Deep_Q_Network)
+    * [Using OpenAI Gym](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/6_OpenAI_gym)
+    * [Double DQN](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/5.1_Double_DQN)
+    * [DQN with Prioitized Experience Replay](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/5.2_Prioritized_Replay_DQN)
+    * [Dueling DQN](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/5.3_Dueling_DQN)
+    * [Policy Gradients](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/7_Policy_gradient_softmax)
+    * [Actor Critic](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/8_Actor_Critic_Advantage)
+    * [Deep Deterministic Policy Gradient](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/9_Deep_Deterministic_Policy_Gradient_DDPG)
+    * [A3C](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/10_A3C)
+    * [Dyna-Q](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/11_Dyna_Q)
+    * [Proximal Policy Optimization (PPO)](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/12_Proximal_Policy_Optimization)
+* [Some of my experiments](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/experiments)
+    * [2D Car](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/experiments/2D_car)
+    * [Robot arm](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/experiments/Robot_arm)
+    * [BipedalWalker](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/experiments/Solve_BipedalWalker)
+    * [LunarLander](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/experiments/Solve_LunarLander)
 
 # Some RL Networks
-### [Deep Q Network](/tree/master/contents/5_Deep_Q_Network)
+### [Deep Q Network](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/5_Deep_Q_Network)
 
-<a href="/tree/master/contents/5_Deep_Q_Network">
+<a href="https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/5_Deep_Q_Network">
     <img class="course-image" src="https://morvanzhou.github.io/static/results/reinforcement-learning/4-3-2.png">
 </a>
 
-### [Double DQN](/tree/master/contents/5.1_Double_DQN)
+### [Double DQN](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/5.1_Double_DQN)
 
-<a href="/tree/master/contents/5.1_Double_DQN">
+<a href="https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/5.1_Double_DQN">
     <img class="course-image" src="https://morvanzhou.github.io/static/results/reinforcement-learning/4-5-3.png">
 </a>
 
-### [Dueling DQN](/tree/master/contents/5.3_Dueling_DQN)
+### [Dueling DQN](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/5.3_Dueling_DQN)
 
-<a href="/tree/master/contents/5.3_Dueling_DQN">
+<a href="https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/5.3_Dueling_DQN">
     <img class="course-image" src="https://morvanzhou.github.io/static/results/reinforcement-learning/4-7-4.png">
 </a>
 
-### [Actor Critic](/tree/master/contents/8_Actor_Critic_Advantage)
+### [Actor Critic](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/8_Actor_Critic_Advantage)
 
-<a href="/tree/master/contents/8_Actor_Critic_Advantage">
+<a href="https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/8_Actor_Critic_Advantage">
     <img class="course-image" src="https://morvanzhou.github.io/static/results/reinforcement-learning/6-1-1.png">
 </a>
 
-### [Deep Deterministic Policy Gradient](/tree/master/contents/9_Deep_Deterministic_Policy_Gradient_DDPG)
+### [Deep Deterministic Policy Gradient](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/9_Deep_Deterministic_Policy_Gradient_DDPG)
 
-<a href="/tree/master/contents/9_Deep_Deterministic_Policy_Gradient_DDPG">
+<a href="https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/9_Deep_Deterministic_Policy_Gradient_DDPG">
     <img class="course-image" src="https://morvanzhou.github.io/static/results/reinforcement-learning/6-2-2.png">
 </a>
 
-### [A3C](/tree/master/contents/10_A3C)
+### [A3C](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/10_A3C)
 
-<a href="/tree/master/contents/10_A3C">
+<a href="https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/10_A3C">
     <img class="course-image" src="https://morvanzhou.github.io/static/results/reinforcement-learning/6-3-2.png">
 </a>
 
-### [Proximal Policy Optimization (PPO)](/tree/master/contents/12_Proximal_Policy_Optimization)
+### [Proximal Policy Optimization (PPO)](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/12_Proximal_Policy_Optimization)
 
-<a href="/tree/master/contents/12_Proximal_Policy_Optimization">
+<a href="https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/12_Proximal_Policy_Optimization">
     <img class="course-image" src="https://morvanzhou.github.io/static/results/reinforcement-learning/6-4-3.png">
 </a>