In a way, Reinforcement Learning is the science of making optimal decisions using experiences. 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. Let's design a simulation of a self-driving cab.
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. The Smartcab's job is to pick up the passenger at one location and drop them off in another. 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.
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. The State Space is the set of all possible situations our taxi could inhabit.
The state should contain useful information the agent needs to make the right action. 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. We can break up the parking lot into a 5x5 grid, which gives us 25 possible taxi locations. These 25 locations are one part of our state space. Notice the current location state of our taxi is coordinate 3, 1.
You'll also notice there are four 4 locations that we can pick up and drop off a passenger: R, G, Y, B or [ 0,0 , 0,4 , 4,0 , 4,3 ] in row, col coordinates. Our illustrated passenger is in location Y and they wish to go to location R. The agent encounters one of the states and it takes an action. This is the action space : the set of all the actions that our agent can take in a given state.
You'll notice in the illustration above, that the taxi cannot perform certain actions in certain states due to walls. In environment's code, we will simply provide a -1 penalty for every wall hit and the taxi won't move anywhere.
This will just rack up penalties causing the taxi to consider going around the wall. Fortunately, OpenAI Gym has this exact environment already built for us. Gym provides different game environments which we can plug into our code and test an agent. The library takes care of API for providing all the information that our agent would require, like possible actions, score, and current state. We just need to focus just on the algorithm part for our agent.
We'll be using the Gym environment called Taxi-V2 , which all of the details explained above were pulled from.
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The objectives, rewards, and actions are all the same. We need to install gym first. Executing the following in a Jupyter notebook should work:. The core gym interface is env , which is the unified environment interface. The following are the env methods that would be quite helpful to us:. Note: We are using the. There is also a 10 point penalty for illegal pick-up and drop-off actions. As verified by the prints, we have an Action Space of size 6 and a State Space of size As you'll see, our RL algorithm won't need any more information than these two things.
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 where:. Recall that the states correspond to a encoding of the taxi's location, the passenger's location, and the destination location.
Reinforcement Learning will learn a mapping of states to the optimal action to perform in that state by exploration , i. The optimal action for each state is the action that has the highest cumulative long-term reward. We can actually take our illustration above, encode its state, and give it to the environment to render in Gym. Recall that we have the taxi at row 3, column 1, our passenger is at location 2, and our destination is location 0.
Using the Taxi-v2 state encoding method, we can do the following:.vitektransportation.com/scripts/voreryz/fyr-december-30.php
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We are using our illustration's coordinates to generate a number corresponding to a state between 0 and , which turns out to be for our illustration's state. Then we can set the environment's state manually with env. You can play around with the numbers and you'll see the taxi, passenger, and destination move around.
We can think of it like a matrix that has the number of states as rows and number of actions as columns, i. Since every state is in this matrix, we can see the default reward values assigned to our illustration's state:.
Note that if our agent chose to explore action two 2 in this state it would be going East into a wall. 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. Since we have our P table for default rewards in each state, we can try to have our taxi navigate just using that.
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 The env. Not good. Our agent takes thousands of timesteps and makes lots of wrong drop offs to deliver just one passenger to the right destination. This is because we aren't learning from past experience.
We can run this over and over, and it will never optimize. The agent has no memory of which action was best for each state, which is exactly what Reinforcement Learning will do for us. We are going to use a simple RL algorithm called Q-learning which will give our agent some memory. Essentially, Q-learning lets the agent use the environment's rewards to learn, over time, the best action to take in a given state.
In our Taxi environment, we have the reward table, P , that the agent will learn from. It does thing by looking receiving a reward for taking an action in the current state, then updating a Q-value to remember if that action was beneficial. The values store in the Q-table are called a Q-values , and they map to a state, action combination. A Q-value for a particular state-action combination is representative of the "quality" of an action taken from that state.
If you bribe, your dog will only perform the behavior if there's a piece of food in front of him first.
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If you reward, then your dog will perform the behavior in hopes of earning that piece of food. And after the learning phase, when that behavior is fluent you can start to phase out the food rewards. For example, if you are teaching your dog to sit on cue, you would start off by reinforcing every sit with a piece of food.
At some point that behavior will become so familiar and easy to your dog that when you say "sit," he will do it without thinking. When he is that good at sitting on cue, you can reward him with food sometimes but not every time. Then you may be able to substitute praise good dog! If he wants you to open the door so he can go out into a fenced yard, he can "earn" that by sitting at the door. Or if you ask him to sit before you put his food bowl down, that is also a life reward. Basically the food is a training tool, and will not be required forever.
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But also keep in mind that nobody goes to one math class and comes out able to solve algebraic equations! Give your dog a lot of practice in a lot of different contexts before deciding that he "knows" how to do something. If I train a lot, won't my dog get fat? Whatever amount you put into your dog for training during the day, you must deduct from his dish at dinner time. If you stuff your dog full of treats all day long, and then feed him his regular meals, he just may get fat! But other than that, you have a great resource for training in that bowl you fill up twice a day.
Instead of just putting the bowl down and letting him eat it all at once, use a portion of it for training. Some trainers give their dogs their entire daily ration in training sessions throughout the day. Also note that dogs don't really work for the taste of food, they work for the smell. If you use very tiny pieces of food, they will be just as happy to work for them as if you offer a big chunk.