Update README.md

Author: sudharsan13296

README.md

@@ -23,23 +23,23 @@ The book starts with an introduction to Reinforcement Learning followed by OpenA
 
 * [1.1. What is Reinforcement Learning?](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/blob/master/01.%20Introduction%20to%20Reinforcement%20Learning/1.1%20What%20is%20Reinforcement%20Learning.ipynb)
 * 1.2. Reinforcement Learning Cycle
-* [1.3. How RL differs from other ML Paradigms?](#)
-* [1.4. Elements of Reinforcement Learning](#)
-* [1.5. Agent Environment Interface](#)
-* [1.6. Types of RL Environments](#)
-* [1.7. Reinforcement Learning Platforms](#)
-* [1.8. Applications of Reinforcement Learning](#)
+* 1.3. How RL differs from other ML Paradigms?
+* 1.4. Elements of Reinforcement Learning
+* 1.5. Agent Environment Interface
+* 1.6. Types of RL Environments
+* 1.7. Reinforcement Learning Platforms
+* 1.8. Applications of Reinforcement Learning
 
 
 
 ### [2. Getting Started with OpenAI and Tensorflow](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/tree/master/02.%20Getting%20Started%20with%20OpenAI%20and%20Tensorflow)
 
-* [2.1. Setting Up Your Machine ](#)
-* [2.2. Installing Anaconda](#)
-* [2.3. Installing Docker](#)
-* [2.4. Installing OpenAI Gym and Universe ](#)
-* [2.5. Common Error Fixes](#)
-* [2.6. OpenAI Gym ](#)
+* 2.1. Setting Up Your Machine 
+* 2.2. Installing Anaconda
+* 2.3. Installing Docker
+* 2.4. Installing OpenAI Gym and Universe 
+* 2.5. Common Error Fixes
+* 2.6. OpenAI Gym 
 * [2.7. Basic Simulations](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/blob/master/02.%20Getting%20Started%20with%20OpenAI%20and%20Tensorflow/2.07%20Basic%20Simulations.ipynb)
 * [2.8. Training a Robot to walk ](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/blob/master/02.%20Getting%20Started%20with%20OpenAI%20and%20Tensorflow/2.08%20Training%20an%20Robot%20to%20Walk.ipynb)
 * [2.9. Building a Video Game Bot](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/blob/master/02.%20Getting%20Started%20with%20OpenAI%20and%20Tensorflow/2.09%20Building%20a%20Video%20Game%20Bot%20.ipynb) 
@@ -50,121 +50,121 @@ The book starts with an introduction to Reinforcement Learning followed by OpenA
 ### [3. Markov Decision Process and Dynamic Programming](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/tree/master/03.%20Markov%20Decision%20Process%20and%20Dynamic%20Programming)
 
 
-* [3.1. Markov Chain and Markov Process ](#)
-* [3.2. Markov Decision Process ](#)
-* [3.3. Rewards and Returns ](#)
-* [3.4. Episodic and Continous Tasks](#)
-* [3.5. Policy Function ](#)
-* [3.6. State Value Function](#)
-* [3.7. State-Action Value Function (Q Function) ](#)
-* [3.8. Bellman Equation and Optimality ](#)
-* [3.9. Deriving Bellman Equation for Value and Q functions](#)
-* [3.10. Solving the Bellman Equation ](#)
-* [3.11. Dynamic Programming ](#)
+* 3.1. Markov Chain and Markov Process 
+* 3.2. Markov Decision Process 
+* 3.3. Rewards and Returns 
+* 3.4. Episodic and Continous Tasks
+* 3.5. Policy Function 
+* 3.6. State Value Function
+* 3.7. State-Action Value Function (Q Function) 
+* 3.8. Bellman Equation and Optimality 
+* 3.9. Deriving Bellman Equation for Value and Q functions
+* 3.10. Solving the Bellman Equation 
+* 3.11. Dynamic Programming 
 * [3.12. Solving Frozen Lake Problem using Value Iteration](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/blob/master/03.%20Markov%20Decision%20Process%20and%20Dynamic%20Programming/3.12%20Value%20Iteration%20-%20Frozen%20Lake%20Problem.ipynb)
 * [3.13. Solving Frozen Lake Problem using Policy Iteration](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/blob/master/03.%20Markov%20Decision%20Process%20and%20Dynamic%20Programming/3.13%20Policy%20Iteration%20-%20Frozen%20Lake%20Problem.ipynb)
 
 
 ### [4. Gaming with Monte Carlo Methods](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/tree/master/04.%20Gaming%20with%20Monte%20Carlo%20Methods)
 
-* [4.1. Monte Carlo Methods](#)
+* 4.1. Monte Carlo Methods
 * [4.2. Estimating Value of Pi Using Monte Carlo](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/blob/master/04.%20Gaming%20with%20Monte%20Carlo%20Methods/4.2%20Estimating%20Value%20of%20Pi%20using%20Monte%20Carlo.ipynb)
-* [4.3. Monte Carlo Prediction](#)
-* [4.4. First visit Monte Carlo](#)
-* [4.5. Every visit Monte Carlo](#)
+* 4.3. Monte Carlo Prediction
+* 4.4. First visit Monte Carlo
+* 4.5. Every visit Monte Carlo
 * [4.6. BlackJack with Monte Carlo](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/blob/master/04.%20Gaming%20with%20Monte%20Carlo%20Methods/4.6%20BlackJack%20with%20First%20visit%20MC.ipynb)
-* [4.7. Monte Carlo Control](#)
-* [4.8. Monte Carlo Exploration Starts](#)
-* [4.9. On Policy Monte Carlo Control](#)
-* [4.10. Off Policy Monte Carlo Control](#)
+* 4.7. Monte Carlo Control](#)
+* 4.8. Monte Carlo Exploration Starts
+* 4.9. On Policy Monte Carlo Control
+* 4.10. Off Policy Monte Carlo Control
 
 
 ### [5. Temporal Difference Learning](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/tree/master/05.%20Temporal%20Difference%20Learning)
 
 
-* [5.1. Temporal Difference Learning](#)
-* [5.2. TD Prediction](#)
-* [5.3. TD Control](#)
-* [5.4. Q Learning](#)
+* 5.1. Temporal Difference Learning
+* 5.2. TD Prediction
+* 5.3. TD Control
+* 5.4. Q Learning
 * [5.5. Solving the Taxi Problem using Q learning](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/blob/master/5.%20Temporal%20Difference%20Learning/05.5%20Taxi%20Problem%20-%20Q%20Learning.ipynb)
-* [5.6. SARSA](#)
+* 5.6. SARSA
 * [5.7. Solving the Taxi Problem using SARSA](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/blob/master/5.%20Temporal%20Difference%20Learning/05.7%20Taxi%20Problem%20-%20SARSA.ipynb)
-* [5.8. Difference Between Q learning and SARSA](#)
+* 5.8. Difference Between Q learning and SARSA
 
 
 ### [6. Multi-Armed Bandit Problem](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/tree/master/06.%20Multi-Armed%20Bandit%20Problem)
 
 
 * [6.1. Multi-armed Bandit Problem](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/blob/master/06.%20Multi-Armed%20Bandit%20Problem/6.1%20MAB%20-%20Various%20Exploration%20Strategies.ipynb)
-* [6.2. Epsilon-Greedy Algorithm](#)
-* [6.3. Softmax Exploration Algorithm](#)
-* [6.4. Upper Confidence Bound Algorithm](#) 
-* [6.5. Thompson Sampling Algorithm](#)
-* [6.6. Applications of MAB](#)
+* 6.2. Epsilon-Greedy Algorithm
+* 6.3. Softmax Exploration Algorithm
+* 6.4. Upper Confidence Bound Algorithm
+* 6.5. Thompson Sampling Algorithm
+* 6.6. Applications of MAB
 * [6.7. Identifying Right Advertisement Banner Using MAB](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/blob/master/06.%20Multi-Armed%20Bandit%20Problem/6.7%20Identifying%20Right%20AD%20Banner%20Using%20MAB.ipynb)
-* [6.8. Contextual Bandits](#)
+* 6.8. Contextual Bandits
 
 
 ### [7. Deep Learning Fundamentals](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/tree/master/07.%20Deep%20Learning%20Fundamentals)
 
-* [7.1. Artificial Neurons](#)
-* [7.2. Artificial Neural Network](#)
-* [7.3. Activation Functions](#)
-* [7.4. Deep Dive into ANN](#)
-* [7.5. Gradient Descent](#)
+* 7.1. Artificial Neurons
+* 7.2. Artificial Neural Network
+* 7.3. Activation Functions
+* 7.4. Deep Dive into ANN
+* 7.5. Gradient Descent
 * [7.6. Neural Networks in Tensorflow](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/blob/master/07.%20Deep%20Learning%20Fundamentals/7.6%20Neural%20Network%20Using%20Tensorflow.ipynb)
-* [7.7. Recurrent Neural Network](#)
-* [7.8. Backpropagation Through Time](#)
-* [7.9. Long Short Term Memory RNN](#)
+* 7.7. Recurrent Neural Network
+* 7.8. Backpropagation Through Time
+* 7.9. Long Short Term Memory RNN
 * [7.10. Generating Song Lyrics using LSTM RNN](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/blob/master/07.%20Deep%20Learning%20Fundamentals/7.10%20Generating%20Song%20Lyrics%20Using%20LSTM%20RNN.ipynb)
-* [7.11. Convolutional Neural Networks](#)
-* [7.12. CNN Architecture](#)
+* 7.11. Convolutional Neural Networks
+* 7.12. CNN Architecture
 * [7.13. Classifying Fashion Products Using CNN](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/blob/master/07.%20Deep%20Learning%20Fundamentals/7.13%20Classifying%20Fashion%20Products%20Using%20CNN.ipynb)
 
 
 ### [8. Atari Games With Deep Q Network](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/tree/master/08.%20Atari%20Games%20with%20DQN)
 
-* [8.1. What is Deep Q network?](#)
-* [8.2. Architecture of DQN](#)
-* [8.3. Convolutional Network](#)
-* [8.4. Experience Replay](#)
-* [8.5. Target Network](#)
-* [8.6. Clipping Rewards](#)
-* [8.7. DQN Algorithm](#)
+* 8.1. What is Deep Q network
+* 8.2. Architecture of DQN
+* 8.3. Convolutional Network
+* 8.4. Experience Replay
+* 8.5. Target Network
+* 8.6. Clipping Rewards
+* 8.7. DQN Algorithm
 * [8.8. Building an Agent to Play Atari Games](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/blob/master/08.%20Atari%20Games%20with%20DQN/8.8%20Building%20an%20Agent%20to%20Play%20Atari%20Games.ipynb)
-* [8.9. Double DQN](#)
-* [8.10. Dueling Architecture](#)
+* 8.9. Double DQN
+* 8.10. Dueling Architecture
 
 
 ### [9. Playing Doom With Deep Recurrent Q Network ](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/tree/master/09.%20Playing%20Doom%20Game%20using%20DRQN)
 
-* [9.1. Deep Recurrent Q Network](#)
-* [9.2. Partially Observable MDP](#)
-* [9.3. Architecture of DRQN](#)
+* 9.1. Deep Recurrent Q Network
+* 9.2. Partially Observable MDP
+* 9.3. Architecture of DRQN
 * [9.4. Basic Doom Game](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/blob/master/09.%20Playing%20Doom%20Game%20using%20DRQN/9.4%20Basic%20Doom%20Game.ipynb)
 * [9.5. Build an Agent to Play Doom Game using DRQN](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/blob/master/09.%20Playing%20Doom%20Game%20using%20DRQN/9.5%20Doom%20Game%20Using%20DRQN.ipynb)
-* [9.6. Deep Attention Recurrent Q Network](#)
+* 9.6. Deep Attention Recurrent Q Network
 
 
 ### [10. Asynchronous Advantage Actor Critic Network ](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/tree/master/10.%20Aysnchronous%20Advantage%20Actor%20Critic%20Network)
 
-* [10.1. Asynchronous Actor Critic Algorithm](#)
-* [10.2. The three A's](#)
-* [10.3. Architecture of A3C](#)
-* [10.4. Working of A3C](#)
+* 10.1. Asynchronous Actor Critic Algorithm
+* 10.2. The three A's
+* 10.3. Architecture of A3C
+* 10.4. Working of A3C
 * [10.5. Drive up the Mountain with A3C](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/blob/master/10.%20Aysnchronous%20Advantage%20Actor%20Critic%20Network/10.5%20Drive%20up%20the%20Mountain%20Using%20A3C.ipynb)
-* [10.6. Visualization in Tensorboard](#)
+* 10.6. Visualization in Tensorboard
 
 
 
 ### [11. Policy Gradients and Optimization](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/tree/master/11.%20Policy%20Gradients%20and%20Optimization)
 
-* [11.1. Policy Gradient ](#)
+* 11.1. Policy Gradient
 * [11.2. Lunar Lander Using Policy Gradient](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/blob/master/11.%20Policy%20Gradients%20and%20Optimization/11.2%20Lunar%20Lander%20Using%20Policy%20Gradients.ipynb)
-* [11.3. Deep Deterministic Policy Gradient ](#)
+* 11.3. Deep Deterministic Policy Gradient 
 * [11.4. Swinging up the Pendulum using DDPG](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/blob/master/11.%20Policy%20Gradients%20and%20Optimization/11.3%20Swinging%20Up%20the%20Pendulum%20Using%20DDPG.ipynb)
-* [11.5. Trust Region Policy Optimization](#)
-* [11.6. Proximal Policy Optimization](#)
+* 11.5. Trust Region Policy Optimizatio
+* 11.6. Proximal Policy Optimization
 
 ### [12. Capstone Project: Car Racing using DQN](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/tree/master/12.%20Capstone%20Project:%20Car%20Racing%20using%20DQN)
 
@@ -177,9 +177,9 @@ The book starts with an introduction to Reinforcement Learning followed by OpenA
 
 ### [13. Recent Advancements and Next Steps](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/tree/master/13.%20Recent%20Advancements%20and%20Next%20Steps)
 
-* [13.1. Imagination Augmented Agents](#)
-* [13.2. Learning From Human Preference](#)
+* 13.1. Imagination Augmented Agents
+* 13.2. Learning From Human Preference
 * [13.3. Deep Q Learning From Demonstrations](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/blob/master/13.%20Recent%20Advancements%20and%20Next%20Steps/13.3%20Deep%20Q%20Learning%20From%20Demonstrations.ipynb)
 * [13.4. Hindsight Experience Replay](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/blob/master/13.%20Recent%20Advancements%20and%20Next%20Steps/13.4%20Hindsight%20Experience%20Replay.ipynb)
-* [13.5. Hierarchical Reinforcement Learning](#)
-* [13.6. Inverse Reinforcement Learning](#)
+* 13.5. Hierarchical Reinforcement Learning
+* 13.6. Inverse Reinforcement Learning