We receive +20 points for a successful drop-off and lose 1 point for every time-step it takes. For example, in the image below we have three people labelled A, B and C. A and B both throw in the correct direction but person A is closer than B and so will have a higher probability of landing the shot. And that’s it, we have our first reinforcement learning environment. Because we have known probabilities, we can actually use model-based methods and will demonstrate this first and can use value-iteration to achieve this via the following formula: Value iteration starts with an arbitrary function V0 and uses the following equations to get the functions for k+1 stages to go from the functions for k stages to go (https://artint.info/html/ArtInt_227.html). All we need is a way to identify a state uniquely by assigning a unique number to every possible state, and RL learns to choose an action number from 0-5 where: Recall that the 500 states correspond to a encoding of the taxi's location, the passenger's location, and the destination location. 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. 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. Reinforcement Learning Tutorial with TensorFlow. In our Taxi environment, we have the reward table, P, that the agent will learn from. Machine Learning From Scratch About. Drop off the passenger to the right location. The agent encounters one of the 500 states and it takes an action. Your Work. Take a look, https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.quiver.html. 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. 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. If you'd like to continue with this project to make it better, here's a few things you can add: Shoot us a tweet @learndatasci with a repo or gist and we'll check out your additions! Reinforcement Learning will learn a mapping of states to the optimal action to perform in that state by exploration, i.e. - $\Large \gamma$ (gamma) is the discount factor ($0 \leq \gamma \leq 1$) - determines how much importance we want to give to future rewards. The values store in the Q-table are called a Q-values, and they map to a (state, action) combination. There's a tradeoff between exploration (choosing a random action) and exploitation (choosing actions based on already learned Q-values). Open AI also has a platform called universe for measuring and training an AI's general intelligence across myriads of games, websites and other general applications. We have discussed a lot about Reinforcement Learning and games. I thought that the session, led by Arthur Juliani, was extremely informative […] The Q-value of a state-action pair is the sum of the instant reward and the discounted future reward (of the resulting state). The major goal is to demonstrate, in a simplified environment, how you can use RL techniques to develop an efficient and safe approach for tackling this problem. 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. Download (48 KB) New Notebook. Our agent takes thousands of timesteps and makes lots of wrong drop offs to deliver just one passenger to the right destination. We will now imagine that the probabilities are unknown to the person and therefore experience is needed to find the optimal actions. This will just rack up penalties causing the taxi to consider going around the wall. Turtle provides an easy and simple interface to build and moves … 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. Note that the Q-table has the same dimensions as the reward table, but it has a completely different purpose. We used normalised integer x and y values so that they must be bounded by -10 and 10. If you have any questions, please feel free to comment below or on the Kaggle pages. The probability of a successful throw is relative to the distance and direction in which it is thrown. A Q-value for a particular state-action combination is representative of the "quality" of an action taken from that state. Update Q-table values using the equation. In environment's code, we will simply provide a -1 penalty for every wall hit and the taxi won't move anywhere. 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. So, our taxi environment has $5 \times 5 \times 5 \times 4 = 500$ total possible states. To balance the random selection slightly between move or throwing actions (as there are only 8 move actions but 360 throwing actions) I decided to give the algorithm a 50/50 chance of moving or throwing then will subsequently pick an action randomly from these. The state should contain useful information the agent needs to make the right action. Author and Editor at LearnDataSci. The env.action_space.sample() method automatically selects one random action from set of all possible actions. To demonstrate this further, we can iterate through a number of throwing directions and create an interactive animation. Teach a Taxi to pick up and drop off passengers at the right locations with Reinforcement Learning. Know more here. not throwing the wrong way) then we can use the following to calculate how good this chosen direction is. But Reinforcement learning is not just limited to games. Let's evaluate the performance of our agent. The aim is for us to find the optimal action in each state by either throwing or moving in a given direction. Now guess what, the next time the dog is exposed to the same situation, the dog executes a similar action with even more enthusiasm in expectation of more food. We evaluate our agents according to the following metrics. There is also a 10 point penalty for illegal pick-up and drop-off actions.". We are assigning ($\leftarrow$), or updating, the Q-value of the agent's current state and action by first taking a weight ($1-\alpha$) of the old Q-value, then adding the learned value. We can break up the parking lot into a 5x5 grid, which gives us 25 possible taxi locations. The dog doesn't understand our language, so we can't tell him what to do. The source code has made it impossible to actually move the taxi across a wall, so if the taxi chooses that action, it will just keep accruing -1 penalties, which affects the long-term reward. The action in our case can be to move in a direction or decide to pickup/dropoff a passenger. Hotness. We can actually take our illustration above, encode its state, and give it to the environment to render in Gym. "Slight" negative because we would prefer our agent to reach late instead of making wrong moves trying to reach to the destination as fast as possible. 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. Reinforcement Learning: Creating a Custom Environment. A more fancy way to get the right combination of hyperparameter values would be to use Genetic Algorithms. 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. These metrics were computed over 100 episodes. 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. And as the results show, our Q-learning agent nailed it! 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. 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. the agent explores the environment and takes actions based off rewards defined in the environment. Because our environment is so simple, it actually converges to the optimal policy within just 10 updates. I can throw the paper in any direction or move one step at a time. We can run this over and over, and it will never optimize. We then dived into the basics of Reinforcement Learning and framed a Self-driving cab as a Reinforcement Learning problem. We emulate a situation (or a cue), and the dog tries to respond in many different ways. Consider the scenario of teaching a dog new tricks. 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. We'll create an infinite loop which runs until one passenger reaches one destination (one episode), or in other words, when the received reward is 20. 2. gamma: The discount factor we use to discount the effect of old actions on the final result. All from scratch! for now, the rewards are also all 0 therefore the value for this first calculation is simply: All move actions within the first update will be calculated similarly. We then used OpenAI's Gym in python to provide us with a related environment, where we can develop our agent and evaluate it. Since we have our P table for default rewards in each state, we can try to have our taxi navigate just using that. The Q-table is a matrix where we have a row for every state (500) and a column for every action (6). The Smartcab's job is to pick up the passenger at one location and drop them off in another. 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. While there, I was lucky enough to attend a tutorial on Deep Reinforcement Learning (Deep RL) from scratch by Unity Technologies. Lastly, I decided to show the change of the optimal policy over each update by exporting each plot and passing into a small animation. As verified by the prints, we have an Action Space of size 6 and a State Space of size 500. Let's see how much better our Q-learning solution is when compared to the agent making just random moves. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Machine Learning; Reinforcement Q-Learning from Scratch in Python with OpenAI Gym. Shared With You. 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. 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. Reinforcement Learning from Scratch: Applying Model-free Methods and Evaluating Parameters in Detail . 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. 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. We don't need to explore actions any further, so now the next action is always selected using the best Q-value: We can see from the evaluation, the agent's performance improved significantly and it incurred no penalties, which means it performed the correct pickup/dropoff actions with 100 different passengers. Note that if our agent chose to explore action two (2) in this state it would be going East into a wall. 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. 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. We execute the chosen action in the environment to obtain the next_state and the reward from performing the action. Reinforcement Learning from Scratch: Applying Model-free Methods and Evaluating Parameters in Detail Introduction. 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))). Lower epsilon value results in episodes with more penalties (on average) which is obvious because we are exploring and making random decisions. When the Taxi environment is created, there is an initial Reward table that's also created, called P. Machine Learning From Scratch. We can think of it like a matrix that has the number of states as rows and number of actions as columns, i.e. This game is going to be a simple paddle and ball game. The problem with Q-earning however is, once the number of states in the environment are very high, it becomes difficult to implement them with Q table as the size would become very, very large. a $states \ \times \ actions$ matrix. 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. more_vert. 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. Therefore, the Q value for this action updates accordingly: 0.444*(R((-5,-5),(50),bin) + gamma*V(bin+))) +, (1–0.444)*(R((-5,-5),(50),bin) + gamma*V(bin-))). We will be applying Q-learning and initialise all state-action pairs with a value of 0 and use the update rule: We give the algorithm the choice to throw in any 360 degree direction (to a whole degree) or to move to any surrounding position of the current one. The agent's performance improved significantly after Q-learning. Each of these programs follow a paradigm of Machine Learning known as Reinforcement Learning. We first show the best action based on throwing or moving by a simple coloured scatter shown below. There are lots of great, easy and free frameworks to get you started in few minutes. By following my work I hope that that others may use this as a basic starting point for learning themselves. I am going to use the inbuilt turtle module in python. 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. Most of you have probably heard of AI learning to play computer games on their own, a … Machine Learning From Scratch About. The process is repeated back and forth until the results converge. 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. For example, the probability when the paper is thrown at a 180 degree bearing (due South) for each x/y position is shown below. If the algorithms throws the paper, the probability of success is calculated for this throw and we simulate whether in this case it was successful and receives a positive terminal reward or was unsuccessful and receives a negative terminal reward. 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. In our previous example, person A is south-west from the bin and therefore the angle was a simple calculation but if we applied the same to say a person placed north-east then this would be incorrect. Note: I have chosen 45 degrees as the boundary but you may choose to change this window or could manually scale the probability calculation to weight the distance of direction measure differently. ... Now, let us write a python class for our environment which we will call a grid. The code for this tutorial series can be found here. Then we observed how terrible our agent was without using any algorithm to play the game, so we went ahead to implement the Q-learning algorithm from scratch. This will eventually cause our taxi to consider the route with the best rewards strung together. GitHub - curiousily/Machine-Learning-from-Scratch: Succinct Machine Learning algorithm implementations from scratch in Python, solving real-world problems (Notebooks and Book). This is because we aren't learning from past experience. 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). 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. Part I: Introduction and Training Loop. osbornep • updated 2 years ago (Version 1) Data Tasks Notebooks (7) Discussion Activity Metadata. The 0-5 corresponds to the actions (south, north, east, west, pickup, dropoff) the taxi can perform at our current state in the illustration. 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. I will continue this in a follow up post and improve these initial results by varying the parameters. We may also want to scale the probability differently for distances. Examples of Logistic Regression, Linear Regression, Decision Trees, K-means clustering, Sentiment Analysis, Recommender Systems, Neural Networks and Reinforcement Learning. After that, we calculate the maximum Q-value for the actions corresponding to the next_state, and with that, we can easily update our Q-value to the new_q_value: Now that the Q-table has been established over 100,000 episodes, let's see what the Q-values are at our illustration's state: The max Q-value is "north" (-1.971), so it looks like Q-learning has effectively learned the best action to take in our illustration's state! Lastly, the overall probability is related to both the distance and direction given the current position as shown before. 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. Ideally, all three should decrease over time because as the agent continues to learn, it actually builds up more resilient priors; A simple way to programmatically come up with the best set of values of the hyperparameter is to create a comprehensive search function (similar to grid search) that selects the parameters that would result in best reward/time_steps ratio. 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 the dog's response is the desired one, we reward them with snacks. 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. Each episode ends naturally if the paper is thrown, the action the algorithm performs is decided by the epsilon-greedy action selection procedure whereby the action is selected randomly with probability epsilon and greedily (current max) otherwise. 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. © 2020 LearnDataSci. You'll notice in the illustration above, that the taxi cannot perform certain actions in certain states due to walls. Better Q-values imply better chances of getting greater rewards.