**Prerequisites: **Q-Learning technique.

** Reinforcement Learning** is a type of Machine Learning paradigms in which a learning algorithm is trained not on preset data but rather based on a feedback system. These algorithms are touted as the future of Machine Learning as these eliminate the cost of collecting and cleaning the data.

In this article, we are going to demonstrate how to implement a basic Reinforcement Learning algorithm which is called the **Q-Learning technique**. In this demonstration, we attempt to teach a bot to reach its destination using the **Q-Learning technique**.

**Step 1: Importing the required libraries**

`import` `numpy as np ` `import` `pylab as pl ` `import` `networkx as nx ` |

*chevron_right*

*filter_none*

**Step 2: Defining and visualising the graph**

`edges ` `=` `[(` `0` `, ` `1` `), (` `1` `, ` `5` `), (` `5` `, ` `6` `), (` `5` `, ` `4` `), (` `1` `, ` `2` `), ` ` ` `(` `1` `, ` `3` `), (` `9` `, ` `10` `), (` `2` `, ` `4` `), (` `0` `, ` `6` `), (` `6` `, ` `7` `), ` ` ` `(` `8` `, ` `9` `), (` `7` `, ` `8` `), (` `1` `, ` `7` `), (` `3` `, ` `9` `)] ` ` ` `goal ` `=` `10` `G ` `=` `nx.Graph() ` `G.add_edges_from(edges) ` `pos ` `=` `nx.spring_layout(G) ` `nx.draw_networkx_nodes(G, pos) ` `nx.draw_networkx_edges(G, pos) ` `nx.draw_networkx_labels(G, pos) ` `pl.show() ` |

*chevron_right*

*filter_none*

**Note: **The above graph may not look the same on reproduction of the code because the networkx library in python produces a random graph from the given edges.

**Step 3: Defining the reward the system for the bot**

`MATRIX_SIZE ` `=` `11` `M ` `=` `np.matrix(np.ones(shape ` `=` `(MATRIX_SIZE, MATRIX_SIZE))) ` `M ` `*` `=` `-` `1` ` ` `for` `point ` `in` `edges: ` ` ` `print` `(point) ` ` ` `if` `point[` `1` `] ` `=` `=` `goal: ` ` ` `M[point] ` `=` `100` ` ` `else` `: ` ` ` `M[point] ` `=` `0` ` ` ` ` `if` `point[` `0` `] ` `=` `=` `goal: ` ` ` `M[point[::` `-` `1` `]] ` `=` `100` ` ` `else` `: ` ` ` `M[point[::` `-` `1` `]]` `=` `0` ` ` `# reverse of point ` ` ` `M[goal, goal]` `=` `100` `print` `(M) ` `# add goal point round trip ` |

*chevron_right*

*filter_none*

**Step 4: Defining some utility functions to be used in the training**

`Q ` `=` `np.matrix(np.zeros([MATRIX_SIZE, MATRIX_SIZE])) ` ` ` `gamma ` `=` `0.75` `# learning parameter ` `initial_state ` `=` `1` ` ` `# Determines the available actions for a given state ` `def` `available_actions(state): ` ` ` `current_state_row ` `=` `M[state, ] ` ` ` `available_action ` `=` `np.where(current_state_row >` `=` `0` `)[` `1` `] ` ` ` `return` `available_action ` ` ` `available_action ` `=` `available_actions(initial_state) ` ` ` `# Chooses one of the available actions at random ` `def` `sample_next_action(available_actions_range): ` ` ` `next_action ` `=` `int` `(np.random.choice(available_action, ` `1` `)) ` ` ` `return` `next_action ` ` ` ` ` `action ` `=` `sample_next_action(available_action) ` ` ` `def` `update(current_state, action, gamma): ` ` ` ` ` `max_index ` `=` `np.where(Q[action, ] ` `=` `=` `np.` `max` `(Q[action, ]))[` `1` `] ` ` ` `if` `max_index.shape[` `0` `] > ` `1` `: ` ` ` `max_index ` `=` `int` `(np.random.choice(max_index, size ` `=` `1` `)) ` ` ` `else` `: ` ` ` `max_index ` `=` `int` `(max_index) ` ` ` `max_value ` `=` `Q[action, max_index] ` ` ` `Q[current_state, action] ` `=` `M[current_state, action] ` `+` `gamma ` `*` `max_value ` ` ` `if` `(np.` `max` `(Q) > ` `0` `): ` ` ` `return` `(np.` `sum` `(Q ` `/` `np.` `max` `(Q)` `*` `100` `)) ` ` ` `else` `: ` ` ` `return` `(` `0` `) ` `# Updates the Q-Matrix according to the path chosen ` ` ` `update(initial_state, action, gamma) ` |

*chevron_right*

*filter_none*

**Step 5: Training and evaluating the bot using the Q-Matrix**

`scores ` `=` `[] ` `for` `i ` `in` `range` `(` `1000` `): ` ` ` `current_state ` `=` `np.random.randint(` `0` `, ` `int` `(Q.shape[` `0` `])) ` ` ` `available_action ` `=` `available_actions(current_state) ` ` ` `action ` `=` `sample_next_action(available_action) ` ` ` `score ` `=` `update(current_state, action, gamma) ` ` ` `scores.append(score) ` ` ` `# print("Trained Q matrix:") ` `# print(Q / np.max(Q)*100) ` `# You can uncomment the above two lines to view the trained Q matrix ` ` ` `# Testing ` `current_state ` `=` `0` `steps ` `=` `[current_state] ` ` ` `while` `current_state !` `=` `10` `: ` ` ` ` ` `next_step_index ` `=` `np.where(Q[current_state, ] ` `=` `=` `np.` `max` `(Q[current_state, ]))[` `1` `] ` ` ` `if` `next_step_index.shape[` `0` `] > ` `1` `: ` ` ` `next_step_index ` `=` `int` `(np.random.choice(next_step_index, size ` `=` `1` `)) ` ` ` `else` `: ` ` ` `next_step_index ` `=` `int` `(next_step_index) ` ` ` `steps.append(next_step_index) ` ` ` `current_state ` `=` `next_step_index ` ` ` `print` `(` `"Most efficient path:"` `) ` `print` `(steps) ` ` ` `pl.plot(scores) ` `pl.xlabel(` `'No of iterations'` `) ` `pl.ylabel(` `'Reward gained'` `) ` `pl.show() ` |

*chevron_right*

*filter_none*

Now, Let’s bring this bot to a more realistic setting. Let us imagine that the bot is a detective and is trying to find out the location of a large drug racket. He naturally concludes that the drug sellers will not sell their products in a location which is known to be frequented by the police and the selling locations are near the location of the drug racket. Also, the sellers leave a trace of their products where they sell it and this can help the detective in finding out the required location. We want to train our bot to find the location using these **Environmental Clues**.

**Step 6: Defining and visualizing the new graph with the environmental clues**

`# Defining the locations of the police and the drug traces ` `police ` `=` `[` `2` `, ` `4` `, ` `5` `] ` `drug_traces ` `=` `[` `3` `, ` `8` `, ` `9` `] ` ` ` `G ` `=` `nx.Graph() ` `G.add_edges_from(edges) ` `mapping ` `=` `{` `0` `:` `'0 - Detective'` `, ` `1` `:` `'1'` `, ` `2` `:` `'2 - Police'` `, ` `3` `:` `'3 - Drug traces'` `, ` ` ` `4` `:` `'4 - Police'` `, ` `5` `:` `'5 - Police'` `, ` `6` `:` `'6'` `, ` `7` `:` `'7'` `, ` `8` `:` `'Drug traces'` `, ` ` ` `9` `:` `'9 - Drug traces'` `, ` `10` `:` `'10 - Drug racket location'` `} ` ` ` `H ` `=` `nx.relabel_nodes(G, mapping) ` `pos ` `=` `nx.spring_layout(H) ` `nx.draw_networkx_nodes(H, pos, node_size ` `=` `[` `200` `, ` `200` `, ` `200` `, ` `200` `, ` `200` `, ` `200` `, ` `200` `, ` `200` `]) ` `nx.draw_networkx_edges(H, pos) ` `nx.draw_networkx_labels(H, pos) ` `pl.show() ` |

*chevron_right*

*filter_none*

**Note: **The above graph may look a bit different from the previous graph but they, in fact, are the same graphs. This is due to the random placement of nodes by the `networkx `

library.

**Step 7: Defining some utility functions for the training process**

`Q ` `=` `np.matrix(np.zeros([MATRIX_SIZE, MATRIX_SIZE])) ` `env_police ` `=` `np.matrix(np.zeros([MATRIX_SIZE, MATRIX_SIZE])) ` `env_drugs ` `=` `np.matrix(np.zeros([MATRIX_SIZE, MATRIX_SIZE])) ` `initial_state ` `=` `1` ` ` `# Same as above ` `def` `available_actions(state): ` ` ` `current_state_row ` `=` `M[state, ] ` ` ` `av_action ` `=` `np.where(current_state_row >` `=` `0` `)[` `1` `] ` ` ` `return` `av_action ` ` ` `# Same as above ` `def` `sample_next_action(available_actions_range): ` ` ` `next_action ` `=` `int` `(np.random.choice(available_action, ` `1` `)) ` ` ` `return` `next_action ` ` ` `# Exploring the environment ` `def` `collect_environmental_data(action): ` ` ` `found ` `=` `[] ` ` ` `if` `action ` `in` `police: ` ` ` `found.append(` `'p'` `) ` ` ` `if` `action ` `in` `drug_traces: ` ` ` `found.append(` `'d'` `) ` ` ` `return` `(found) ` ` ` ` ` `available_action ` `=` `available_actions(initial_state) ` `action ` `=` `sample_next_action(available_action) ` ` ` `def` `update(current_state, action, gamma): ` ` ` `max_index ` `=` `np.where(Q[action, ] ` `=` `=` `np.` `max` `(Q[action, ]))[` `1` `] ` ` ` `if` `max_index.shape[` `0` `] > ` `1` `: ` ` ` `max_index ` `=` `int` `(np.random.choice(max_index, size ` `=` `1` `)) ` ` ` `else` `: ` ` ` `max_index ` `=` `int` `(max_index) ` ` ` `max_value ` `=` `Q[action, max_index] ` ` ` `Q[current_state, action] ` `=` `M[current_state, action] ` `+` `gamma ` `*` `max_value ` ` ` `environment ` `=` `collect_environmental_data(action) ` ` ` `if` `'p'` `in` `environment: ` ` ` `env_police[current_state, action] ` `+` `=` `1` ` ` `if` `'d'` `in` `environment: ` ` ` `env_drugs[current_state, action] ` `+` `=` `1` ` ` `if` `(np.` `max` `(Q) > ` `0` `): ` ` ` `return` `(np.` `sum` `(Q ` `/` `np.` `max` `(Q)` `*` `100` `)) ` ` ` `else` `: ` ` ` `return` `(` `0` `) ` `# Same as above ` `update(initial_state, action, gamma) ` ` ` `def` `available_actions_with_env_help(state): ` ` ` `current_state_row ` `=` `M[state, ] ` ` ` `av_action ` `=` `np.where(current_state_row >` `=` `0` `)[` `1` `] ` ` ` ` ` `# if there are multiple routes, dis-favor anything negative ` ` ` `env_pos_row ` `=` `env_matrix_snap[state, av_action] ` ` ` ` ` `if` `(np.` `sum` `(env_pos_row < ` `0` `)): ` ` ` `# can we remove the negative directions from av_act? ` ` ` `temp_av_action ` `=` `av_action[np.array(env_pos_row)[` `0` `]>` `=` `0` `] ` ` ` `if` `len` `(temp_av_action) > ` `0` `: ` ` ` `av_action ` `=` `temp_av_action ` ` ` `return` `av_action ` `# Determines the available actions according to the environment ` |

*chevron_right*

*filter_none*

**Step 8: Visualising the Environmental matrices**

`scores ` `=` `[] ` `for` `i ` `in` `range` `(` `1000` `): ` ` ` `current_state ` `=` `np.random.randint(` `0` `, ` `int` `(Q.shape[` `0` `])) ` ` ` `available_action ` `=` `available_actions(current_state) ` ` ` `action ` `=` `sample_next_action(available_action) ` ` ` `score ` `=` `update(current_state, action, gamma) ` ` ` `# print environmental matrices ` `print` `(` `'Police Found'` `) ` `print` `(env_police) ` `print` `('') ` `print` `(` `'Drug traces Found'` `) ` `print` `(env_drugs) ` |

*chevron_right*

*filter_none*

**Step 9: Training and evaluating the model**

`scores ` `=` `[] ` `for` `i ` `in` `range` `(` `1000` `): ` ` ` `current_state ` `=` `np.random.randint(` `0` `, ` `int` `(Q.shape[` `0` `])) ` ` ` `available_action ` `=` `available_actions_with_env_help(current_state) ` ` ` `action ` `=` `sample_next_action(available_action) ` ` ` `score ` `=` `update(current_state, action, gamma) ` ` ` `scores.append(score) ` ` ` `pl.plot(scores) ` `pl.xlabel(` `'Number of iterations'` `) ` `pl.ylabel(` `'Reward gained'` `) ` `pl.show() ` |

*chevron_right*

*filter_none*

The example taken above was a very basic one and many practical examples like **Self Driving Cars** involve the concept of Game Theory.

Attention geek! Strengthen your foundations with the **Python Programming Foundation** Course and learn the basics.

To begin with, your interview preparations Enhance your Data Structures concepts with the **Python DS** Course.

## Recommended Posts:

- Genetic Algorithm for Reinforcement Learning : Python implementation
- Epsilon-Greedy Algorithm in Reinforcement Learning
- Upper Confidence Bound Algorithm in Reinforcement Learning
- Reinforcement learning
- SARSA Reinforcement Learning
- Introduction to Thompson Sampling | Reinforcement Learning
- Neural Logic Reinforcement Learning - An Introduction
- Expected SARSA in Reinforcement Learning
- Learning Model Building in Scikit-learn : A Python Machine Learning Library
- ML | Types of Learning – Supervised Learning
- Introduction to Multi-Task Learning(MTL) for Deep Learning
- Artificial intelligence vs Machine Learning vs Deep Learning
- Learning to learn Artificial Intelligence | An overview of Meta-Learning
- How to Start Learning Machine Learning?
- Difference Between Artificial Intelligence vs Machine Learning vs Deep Learning
- Need of Data Structures and Algorithms for Deep Learning and Machine Learning
- Box Blur Algorithm - With Python implementation
- Page Rank Algorithm and Implementation
- Implementation of Perceptron Algorithm for NOT Logic Gate
- Implementation of Perceptron Algorithm for AND Logic Gate with 2-bit Binary Input

If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.

Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.