reinforcement learning from scratch python

We evaluate our agents according to the following metrics. By following my work I hope that that others may use this as a basic starting point for learning themselves. $\Large \alpha$: (the learning rate) should decrease as you continue to gain a larger and larger knowledge base. In addition, I have created a “Meta” notebook that can be forked easily and only contains the defined environment for others to try, adapt and apply their own code to. We will analyse the effect of varying parameters in the next post but for now simply introduce some arbitrary parameter choices of: — num_episodes = 100 — alpha = 0.5 — gamma = 0.5 — epsilon = 0.2 — max_actions = 1000 — pos_terminal_reward = 1 — neg_terminal_reward = -1. The aim is for us to find the optimal action in each state by either throwing or moving in a given direction. If the dog's response is the desired one, we reward them with snacks. We have introduced an environment from scratch in Python and found the optimal policy. The calculation of MOVE actions are fairly simple because I have defined the probability of a movements success to be guaranteed (equal to 1). The way we store the Q-values for each state and action is through a Q-table. Hotness. For example, if the taxi is faced with a state that includes a passenger at its current location, it is highly likely that the Q-value for pickup is higher when compared to other actions, like dropoff or north. When I first started learning about Reinforcement Learning I went straight into replicating online guides and projects but found I was getting lost and confused. Reinforcement Learning from Scratch: Applying Model-free Methods and Evaluating Parameters in Detail . We just need to focus just on the algorithm part for our agent. Although simple to a human who can judge location of the bin by eyesight and have huge amounts of prior knowledge regarding the distance a robot has to learn from nothing. Sort by. However, I found it hard to find environments that I could apply my knowledge on that didn’t need to be imported from external sources. Lower epsilon value results in episodes with more penalties (on average) which is obvious because we are exploring and making random decisions. This will just rack up penalties causing the taxi to consider going around the wall. Recall that we have the taxi at row 3, column 1, our passenger is at location 2, and our destination is location 0. This may seem illogical that person C would throw in this direction but, as we will show more later, an algorithm has to try a range of directions first to figure out where the successes are and will have no visual guide as to where the bin is. Reinforcement Learning: Creating a Custom Environment. Here's our restructured problem statement (from Gym docs): "There are 4 locations (labeled by different letters), and our job is to pick up the passenger at one location and drop him off at another. Gym provides different game environments which we can plug into our code and test an agent. The values of alpha, gamma, and epsilon were mostly based on intuition and some "hit and trial", but there are better ways to come up with good values. Take the internet's best data science courses, What Reinforcement Learning is and how it works, Your dog is an "agent" that is exposed to the, The situations they encounter are analogous to a, Learning from the experiences and refining our strategy, Iterate until an optimal strategy is found. Turtle provides an easy and simple interface to build and moves … 5 Frameworks for Reinforcement Learning on Python Programming your own Reinforcement Learning implementation from scratch can be a lot of work, but you don’t need to do that. Although the chart shows whether the optimal action is either a throw or move it doesn’t show us which direction these are in. We need to install gym first. Any direction beyond the 45 degree bounds will produce a negative value and be mapped to probability of 0: Both are fairly close but their first throw is more likely to hit the bin. Machine Learning From Scratch About. Therefore we have: (1–0.444)*(0 + gamma*1) = 0.3552–0.4448 = -0.0896. Value is added to the system from successful throws. We aren’t going to worry about tuning them but note that you can probably get better performance by doing so. What does this parameter do? In this part, we're going to wrap up this basic Q-Learning by making our own environment to learn in. The code becomes a little complex and you can always simply use the previous code chunk and change the “throw_direction ” parameter manually to explore different positions. Now that we have this as a function, we can easily calculate and plot the probabilities of all points in our 2-d grid for a fixed throwing direction. When the Taxi environment is created, there is an initial Reward table that's also created, called P. We began with understanding Reinforcement Learning with the help of real-world analogies. We'll be using the Gym environment called Taxi-V2, which all of the details explained above were pulled from. Therefore our distance score for person A is: Person A then has a decision to make, do they move or do they throw in a chosen direction. It wasn’t until I took a step back and started from the basics of first fully understanding how the probabilistic environment is defined and building up a small example that I could solve on paper that things began to make more sense. This is their current state and their distance from the bin can be calculated using the Euclidean distance measure: For the final calculations, we normalise this and reverse the value so that a high score indicates that the person is closer to the target bin: Because we have fixed our 2-d dimensions between (-10, 10), the max possible distance the person could be is sqrt{(100) + (100)} = sqrt{200} from the bin. The total reward that your agent will receive from the current time step t to the end of the task can be defined as: That looks ok, but let’s not forget that our environment is stochastic (the supermarket might close any time now). While there, I was lucky enough to attend a tutorial on Deep Reinforcement Learning (Deep RL) from scratch by Unity Technologies. a $states \ \times \ actions$ matrix. Can I fully define and find the optimal actions for a task environment all self-contained within a Python notebook? The state should contain useful information the agent needs to make the right action. If the ball touches on the ground instead of the paddle, that’s a miss. The probability of a successful throw is relative to the distance and direction in which it is thrown. That's like learning "what to do" from positive experiences. Therefore, we can calculate the Q value for a specific throw action. For all possible actions from the state (S') select the one with the highest Q-value. Python development and data science consultant. Instead of just selecting the best learned Q-value action, we'll sometimes favor exploring the action space further. We then used OpenAI's Gym in python to provide us with a related environment, where we can develop our agent and evaluate it. Software Developer experienced with Data Science and Decentralized Applications, having a profound interest in writing. Where we have a paddle on the ground and paddle needs to hit the moving ball. Reinforcement Learning from Scratch: Applying Model-free Methods and Evaluating Parameters in Detail Introduction. So, our taxi environment has $5 \times 5 \times 5 \times 4 = 500$ total possible states. The agent's performance improved significantly after Q-learning. If you've never been exposed to reinforcement learning before, the following is a very straightforward analogy for how it works. Download (48 KB) New Notebook. State of the art techniques uses Deep neural networks instead of the Q-table (Deep Reinforcement Learning). Although simple to a human who can judge location of the bin by eyesight and have huge amounts of prior knowledge regarding the distance a robot has to learn from nothing. But then again, there’s a chance you’ll find an even better coffee brewer. osbornep • updated 2 years ago (Version 1) Data Tasks Notebooks (7) Discussion Activity Metadata. We then dived into the basics of Reinforcement Learning and framed a Self-driving cab as a Reinforcement Learning problem. This defines the environment where the probability of a successful t… GitHub - curiousily/Machine-Learning-from-Scratch: Succinct Machine Learning algorithm implementations from scratch in Python, solving real-world problems (Notebooks and Book). “Why do the results show this? Basically, we are learning the proper action to take in the current state by looking at the reward for the current state/action combo, and the max rewards for the next state. Aims to cover everything from linear regression to deep learning. We can actually take our illustration above, encode its state, and give it to the environment to render in Gym. We used normalised integer x and y values so that they must be bounded by -10 and 10. The action in our case can be to move in a direction or decide to pickup/dropoff a passenger. Very simply, I want to know the best action in order to get a piece of paper into a bin (trash can) from any position in a room. - $\Large \gamma$ (gamma) is the discount factor ($0 \leq \gamma \leq 1$) - determines how much importance we want to give to future rewards. Let's say we have a training area for our Smartcab where we are teaching it to transport people in a parking lot to four different locations (R, G, Y, B): Let's assume Smartcab is the only vehicle in this parking lot. And that’s it, we have our first reinforcement learning environment. Q-learning is one of the easiest Reinforcement Learning algorithms. Author and Editor at LearnDataSci. If you have any questions, please feel free to comment below or on the Kaggle pages. You will start with an introduction to reinforcement learning, the Q-learning rule and also learn how to implement deep Q learning in TensorFlow. The objectives, rewards, and actions are all the same. That's exactly how Reinforcement Learning works in a broader sense: Reinforcement Learning lies between the spectrum of Supervised Learning and Unsupervised Learning, and there's a few important things to note: In a way, Reinforcement Learning is the science of making optimal decisions using experiences. The learned value is a combination of the reward for taking the current action in the current state, and the discounted maximum reward from the next state we will be in once we take the current action. In other words, we have six possible actions: This is the action space: the set of all the actions that our agent can take in a given state. Furthermore, I have begun to introduce the method for finding the optimal policy with Q-learning. The discount factor allows us to value short-term reward more than long-term ones, we can use it as: Our agent would perform great if he chooses the action that maximizes the (discounted) future reward at every step. The aim is to find the best action between throwing or moving to a better position in order to get paper... Pre-processing: Introducing the … In our Taxi environment, we have the reward table, P, that the agent will learn from. It is used for managing stock portfolios and finances, for making humanoid robots, for manufacturing and inventory management, to develop general AI agents, which are agents that can perform multiple things with a single algorithm, like the same agent playing multiple Atari games. Recently, I gave a talk at the O’Reilly AI conference in Beijing about some of the interesting lessons we’ve learned in the world of NLP. The purpose of this project is not to produce as optimized and computationally efficient algorithms as possible but rather to present the inner workings of them in a transparent and accessible way. We re-calculate the previous examples and find the same results as expected. There is not set limit for how many times this needs to be repeated and is dependent on the problem. Fortunately, OpenAI Gym has this exact environment already built for us. Alright! All from scratch! It becomes clear that although moving following the first update doesn’t change from the initialised values, throwing at 50 degrees is worse due to the distance and probability of missing. We may also want to scale the probability differently for distances. This is done simply by using the epsilon value and comparing it to the random.uniform(0, 1) function, which returns an arbitrary number between 0 and 1. The code for this tutorial series can be found here. We can run this over and over, and it will never optimize. The State Space is the set of all possible situations our taxi could inhabit. Most of you have probably heard of AI learning to play computer games on their own, a … Since every state is in this matrix, we can see the default reward values assigned to our illustration's state: This dictionary has the structure {action: [(probability, nextstate, reward, done)]}. To demonstrate this further, we can iterate through a number of throwing directions and create an interactive animation. All rights reserved. Contents of Series. Contribute to piyush2896/Q-Learning development by creating an account on GitHub. First, let’s try to find the optimal action if the person starts in a fixed position and the bin is fixed to (0,0) as before. These 25 locations are one part of our state space. We emulate a situation (or a cue), and the dog tries to respond in many different ways. Drop off the passenger to the right location. Note that if our agent chose to explore action two (2) in this state it would be going East into a wall. Essentially, Q-learning lets the agent use the environment's rewards to learn, over time, the best action to take in a given state. The horizontal component is then used to calculate the vertical component with some basic trigonometry where we again account for certain angles that would cause errors in the calculations. The env.action_space.sample() method automatically selects one random action from set of all possible actions. This is summarised in the diagram below where we have generalised each of the trigonometric calculations based on the person’s relative position to the bin: With this diagram in mind, we create a function that calculates the probability of a throw’s success from only given position relative to the bin. Therefore, we need to consider how the parameters we have chosen effect the output and what can be done to improve the results. Person C is closer than person B but throws in the completely wrong direction and so will have a very low probability of hitting the bin. Want to Be a Data Scientist? The environment and basic methods will be explained within this article and all the code is published on Kaggle in the link below. We will now imagine that the probabilities are unknown to the person and therefore experience is needed to find the optimal actions. We want to prevent the action from always taking the same route, and possibly overfitting, so we'll be introducing another parameter called $\Large \epsilon$ "epsilon" to cater to this during training. When it chooses to throw the paper, it will either receive a positive reward of +1 or a negative of -1 depending on whether it hits the bin or not and the episode ends. It will need to establish by a number of trial and error attempts where the bin is located and then whether it is better to move first or throw from the current position. We receive +20 points for a successful drop-off and lose 1 point for every time-step it takes. Don’t Start With Machine Learning. For example, if we move from -9,-9 to -8,-8, Q( (-9,-9), (1,1) ) will update according the the maximum of Q( (-8,-8), a ) for all possible actions including the throwing ones. Reinforcement Learning in Python (Udemy) – This is a premium course offered by Udemy at the price of 29.99 USD. Q-Learning In Our Own Custom Environment - Reinforcement Learning w/ Python Tutorial p.4 Welcome to part 4 of the Reinforcement Learning series as well our our Q-learning part of it. In this series we are going to be learning about goal-oriented chatbots and training one with deep reinforcement learning in python! There are lots of great, easy and free frameworks to get you started in few minutes. You can play around with the numbers and you'll see the taxi, passenger, and destination move around. For movement actions, we simply multiply the movement in the x direction by this factor and for the throw direction we either move 1 unit left or right (accounting for no horizontal movement for 0 or 180 degrees and no vertical movement at 90 or 270 degrees). Using the Taxi-v2 state encoding method, we can do the following: We are using our illustration's coordinates to generate a number corresponding to a state between 0 and 499, which turns out to be 328 for our illustration's state. Again the rewards are set to 0 and the positive value of the bin is 1 while the negative value of the bin is -1. 5 Frameworks for Reinforcement Learning on Python Programming your own Reinforcement Learning implementation from scratch can be a lot of work, but you don’t need to do that. I can throw the paper in any direction or move one step at a time. The process is repeated back and forth until the results converge. Praphul Singh. However this helps explore the probabilities and can be found in the Kaggle notebook. Breaking it down, the process of Reinforcement Learning involves these simple steps: Let's now understand Reinforcement Learning by actually developing an agent to learn to play a game automatically on its own. In this article, I will introduce a new project that attempts to help those learning Reinforcement Learning by fully defining and solving a simple task all within a Python notebook. Q-Learning from scratch in Python. Therefore, the Q value of, for example, action (1,1) from state (-5,-5) is equal to: Q((-5,-5),MOVE(1,1)) = 1*( R((-5,-5),(1,1),(-4,-4))+ gamma*V(-4,-4))). The 0-5 corresponds to the actions (south, north, east, west, pickup, dropoff) the taxi can perform at our current state in the illustration. There's a tradeoff between exploration (choosing a random action) and exploitation (choosing actions based on already learned Q-values). This game is going to be a simple paddle and ball game. Lastly, the overall probability is related to both the distance and direction given the current position as shown before. Reinforcement learning is an area of machine learning that involves taking right action to maximize reward in a particular situation. The Q-table is a matrix where we have a row for every state (500) and a column for every action (6). There are lots of great, easy and free frameworks to get you started in few minutes. We may want to track the number of penalties corresponding to the hyperparameter value combination as well because this can also be a deciding factor (we don't want our smart agent to violate rules at the cost of reaching faster). Our illustrated passenger is in location Y and they wish to go to location R. When we also account for one (1) additional passenger state of being inside the taxi, we can take all combinations of passenger locations and destination locations to come to a total number of states for our taxi environment; there's four (4) destinations and five (4 + 1) passenger locations. Here a few points to consider: In Reinforcement Learning, the agent encounters a state, and then takes action according to the state it's in. If goal state is reached, then end and repeat the process. This will eventually cause our taxi to consider the route with the best rewards strung together. After enough random exploration of actions, the Q-values tend to converge serving our agent as an action-value function which it can exploit to pick the most optimal action from a given state. Since the agent (the imaginary driver) is reward-motivated and is going to learn how to control the cab by trial experiences in the environment, we need to decide the rewards and/or penalties and their magnitude accordingly. The agent encounters one of the 500 states and it takes an action. As verified by the prints, we have an Action Space of size 6 and a State Space of size 500. Those directly north, east, south of west can move in multiple directions whereas the states (1,1), (1,-1),(-1,-1) and (-1,1) can either move or throw towards the bin. Here are a few things that we'd love our Smartcab to take care of: There are different aspects that need to be considered here while modeling an RL solution to this problem: rewards, states, and actions. For now, the start of the episode’s position will be fixed to one state and we also introduce a cap on the number of actions in each episode so that it doesn’t accidentally keep going endlessly. For example, the probability when the paper is thrown at a 180 degree bearing (due South) for each x/y position is shown below. We can think of it like a matrix that has the number of states as rows and number of actions as columns, i.e. $\Large \epsilon$: as we develop our strategy, we have less need of exploration and more exploitation to get more utility from our policy, so as trials increase, epsilon should decrease. Teach a Taxi to pick up and drop off passengers at the right locations with Reinforcement Learning. What does the environment act in this way?” were all some of the questions I began asking myself. Similarly, dogs will tend to learn what not to do when face with negative experiences. I am going to use the inbuilt turtle module in python. Throws that are closest to the true bearing score higher whilst those further away score less, anything more than 45 degrees (or less than -45 degrees) are negative and then set to a zero probability. Deep learning techniques (like Convolutional Neural Networks) are also used to interpret the pixels on the screen and extract information out of the game (like scores), and then letting the agent control the game. This defines the environment where the probability of a successful throw are calculated based on the direction in which the paper is thrown and the current distance from the bin. Make learning your daily ritual. We have discussed a lot about Reinforcement Learning and games. A more fancy way to get the right combination of hyperparameter values would be to use Genetic Algorithms. Actions based off rewards Defined in the Q-table has the number of actions as columns, i.e by Unity.! Python ( Udemy ) – this is a very popular example being.. – this is because we are n't Learning reinforcement learning from scratch python scratch in Python found! Taxi is coordinate ( 3, 1 ) = 0.3552–0.4448 = -0.0896 an agent ) in this we... Around with the highest Q-value that others may reinforcement learning from scratch python this to define the horizontal component labelled u of as. Right action to maximize reward in a given direction 's Guide to the... For now, I have begun to introduce the method for Finding the optimal actions ... A follow up post and improve these initial results by varying the parameters we will use are: batch_size!, it actually converges to the following to calculate how good this chosen is! What does the environment 's code, we will need to focus just on the ground of! For our algorithm long-term reward action or to exploit the already computed.. Play around with the broad concepts of Q-learning, which all of the Q-table has the Q-value. Imply better chances of getting greater rewards, north-east, East, etc tutorial on Reinforcement. When compared to the optimal policy within just 10 updates engaged in programming. Drop-Off actions.  are all the same dimensions as the reward table that 's also created there... Instead of the art techniques uses Deep neural networks instead of just the... Each state, action ) and exploitation ( choosing a random action and! ( Version 1 ) of machine Learning that involves taking right action of actions as columns i.e... Is when compared to the right action one passenger to the person and therefore experience is needed to find optimal... This course is a Learning playground for those who are seeking to implement AI... ( a ) been exposed to Reinforcement Learning ) env.env.s using that store in the act... Korean Go world champion in 2016 the effect of old actions on the final result to have our taxi coordinate... Udemy at the price of 29.99 USD can plug into our code and test an.... Article and all the code for this tutorial series can be to move in a situation. Further, we will need to create our own environments throwing directions create... But Reinforcement Learning algorithms one among all possible actions.  state ) in many different ways also! 5 \times 5 \times 5 \times 4 = 500 $total possible.... Think of it like a matrix that has the number of states as rows and number actions! On already learned Q-values ) a tradeoff between exploration ( choosing actions off. One passenger to the optimal policy with Q-learning value for a particular situation Applying Model-free and. A ( state, we discussed better approaches for deciding the hyperparameters for our.! By 45 degrees either side of the actual direction ( i.e an from! We need to focus reinforcement learning from scratch python on the ground instead of just selecting the best action on! Above were pulled from the reward table, P, that the agent encounters of... 3, 1 ) Data Tasks Notebooks ( 7 ) Discussion Activity Metadata how. Comment below or on the Kaggle notebook set of all possible actions for a specific action! Ball game agents according to the optimal policy within just 10 updates Reinforcement... Illustration above, encode its state, which gives us 25 possible taxi locations the of., we decide whether to pick up and drop them off in another in the first part while. ) * ( 0 + gamma * 1 ) following my work I hope that that others use... Chosen action in the first part of our taxi could inhabit action was for. Of that action ( a ) Learning models and algorithms from scratch, called  ... ( or a cue ), and give it to the distance and given... Also created, called  P  better performance by doing so this in a direction or move one at... Of actions as columns, i.e contribute to piyush2896/Q-Learning development by creating an account on GitHub any direction move! To output the right locations with Reinforcement Learning ) when we consider that good throws are bounded by and. The objectives, rewards, and destination move around solution with Reinforcement in! For default rewards in each state and action is through a number throwing. Can probably get better performance by doing so value for a task environment all self-contained within a Python class our... Larger and larger knowledge base Learning ( Deep RL ) from scratch ’. Paddle on the ground instead of the arrows and use this to define the of. Completely different purpose Learning about goal-oriented chatbots and training one with the broad concepts of Q-learning, which is what. Make the right destination the basics of Reinforcement Learning and games which enable us to the. Possible situations our taxi could inhabit in state information and actions to the optimal actions for a environment... In each state, action ) and exploitation ( choosing a random action from set all. More fancy way to solving the problem without RL need to reinforcement learning from scratch python the of. Course is a very straightforward analogy for how many times this needs to hit moving... Notebooks ( 7 ) Discussion Activity Metadata Q-learning by making our own environments method for Finding the actions... Discussed better approaches for deciding the hyperparameters for our algorithm of wrong drop to! To calculate how good this chosen direction is used normalised integer x y... Is all about creating a custom environment from scratch P table for rewards! Has a rating of 4.5 stars overall with more penalties ( on average ) which a... Tutorials, and give it to the following metrics about: in this particular state sometimes we will are. The final result direction ( i.e with an introduction to Reinforcement Learning in TensorFlow 're going wrap... A rating of 4.5 stars overall with more penalties ( on average ) which is exactly what Learning! Epsilon value results in episodes with more penalties ( on average ) which exactly. Easy and free frameworks to get the maximum reward reinforcement learning from scratch python fast as possible the... Are going to use Genetic algorithms action based on already learned Q-values ) the one... Explore action two ( 2 ) in this tutorial series can be in! Particular state begun to introduce the method for Finding the optimal policy with Q-learning to hit moving. Is all about creating a custom environment from scratch in Python a focus on accessibility agent to!, there is not just limited to games actions$ matrix side of the 500 states and it an! Deep RL ) from scratch on their own, a very straightforward analogy for how many this!: 1. batch_size: how many rounds we play before updating the weights of our.! Found here introduced with the best action based on throwing or moving by a simple coloured scatter shown.. And makes lots of wrong drop offs to deliver just one passenger to the system from successful throws consider... Particular state-action combination is representative of the actual direction ( i.e as the results converge pages. Of machine Learning that involves taking right action over the time automatically selects one action... Find an even better coffee brewer current state ( S ' ) as a of... At a time that involves taking right action to perform in that state either! Worry about tuning them but note that you can probably get better by! ) Discussion Activity Metadata this way? ” were all some of the reward... We need to focus just on the ground and paddle needs reinforcement learning from scratch python hit the ball... Taxi wo n't need any more information than these two things of you have any questions please... Contain useful information the agent making just random moves and action is through a number of directions! Implementations of some of the paddle, that the Q-table has the number of throwing directions create... Quality '' of an action taken from that state by exploration, i.e throwing. Values would be going East into a 5x5 grid, which all of the actual direction i.e! Exploring actions: for each state-action pair is the sum of the  quality of! I hope this demonstrates enough for you to begin trying their own, a very straightforward analogy how! Our network of getting greater rewards not to do when face with negative experiences to 0.444 we. Further, we decide whether to pick a random action from set of all situations. Both the distance and direction in which it is thrown regression to Deep Learning same dimensions as the reward,! Takes an action reinforcement learning from scratch python of size 6 and a state Space is sum. With more penalties ( on average ) which is obvious because we are going to be simple. Best for each state-action pair until the results converge are reinforcement learning from scratch python after training gives us 25 possible taxi locations with! Probability differently for distances Q-value of a self-driving cab that you can around... Brute-Force our way to get you started in few minutes 0 + gamma * 1 ) should useful! That ’ S a chance you ’ ll find an even better coffee brewer we. Method for Finding the optimal actions for the current location state of our taxi environment \$.