You can run the code for this section in this jupyter notebook link. The key here is we want to get to G without falling into the hole H in the shortest amount of time. In this game, we know our transition probability function and reward function, essentially the whole environment, allowing us to turn this game into a simple planning problem via dynamic programming through 4 simple functions: 1 policy evaluation 2 policy improvement 3 policy iteration or 4 value iteration.
Deep Learning Wizard. Dynamic Programming Run Jupyter Notebook You can run the code for this section in this jupyter notebook link. Observation space State space print env. Sampling state space We should expect to see 15 possible grids from 0 to 15 when we uniformly randomly sample from our observation space for i in range 10 : print env.
Action space Action space print env. Random sampling of actions We should expect to see 4 actions when we uniformly randomly sample: 1. LEFT: 0 2. Initial state This sets the initial state at S, our starting point We can render the environment to see where we are on the 4x4 frozenlake gridworld env. Go right?
Go right 10 times? Intuitively when we are moving on a frozen lake, some times when we want to walk one direction we may end up in another direction as it's slippery Setting seed here of the environment so you can reproduce my results, otherwise stochastic policy will yield different results for each run env. DiscreteEnv : """ Winter is here. You and your friends were tossing around a frisbee at the park when you made a wild throw that left the frisbee out in the middle of the lake.
The water is mostly frozen, but there are a few holes where the ice has melted. If you step into one of those holes, you'll fall into the freezing water. At this time, there's an international frisbee shortage, so it's absolutely imperative that you navigate across the lake and retrieve the disc.
However, the ice is slippery, so you won't always move in the direction you intend. You receive a reward of 1 if you reach the goal, and zero otherwise.
OpenAI gym tutorial
Policy plot import seaborn as sns import matplotlib. Returns V comprising values of states under given policy. Args: env gym.This article is the first of a long serie of articles about reinforcement learning.
This serie is intented for readers who already have some notions of machine learning and are confident with Python and TensorFlow.
In machine learning and particularly in deep learning, once we have implemented our model CNN, RNN, … what we need to test its quality is some data. Indeed, we feed our model with our data and it will learn based on the data it is seeing. Instead, we need an environment with a set of rules and a set of functions.
For example a chessboard and all the rules of the chess game form the environment. Creating the environment is quite complex and bothersome. This is where OpenAI Gym comes into play. OpenAI Gym provides a set of virtual environments that you can use to test the quality of your agents. If you type pip freeze you should see the gym package.
I will only focus on the FrozenLake-V0 environment in this article. Figure 2 represents a more friendly visualization of the FrozenLake-v0 board game. To load the FrozenLake-V0 environment, you can just write, in python:. That means that the FrozenLake-V0 environment has 4 discrete actions and 16 discrete states. Hence env.The promises and pitfalls of Stochastic Gradient Langevin Dynamics - Eric Moulines
For example:. Another example to be sure you understood how to interpret the information returned by env. The curious reader can see how the environment is implemented when the tiles are slippery the environment is stochastic here. What if we want the environment to be deterministic? To do so we can customize the environment and it is exactly the topic of this section. That we want the tiles to be non slippery deterministic environment and that this newly environment is registered under the name Deterministic-4x4-FrozenLake-v0.
How can I know which arguments kwargs are available and how can I know which values are correct? To know this information, the best way is to look at the source code here. What if I want to create my own environment? We can also see that the FrozenLakeEnv accepts a desc stands for description which allow us to create our own environment.
I define my own environment using a list that contains FGH or S characters that we already seen previously :. What if I want to change the reward or the probability in a certain action-state pair?
Well, If you want to achieve such a thing, the easiest way to do it, is to create your own class that inherits from gym. To do so we need to create a new python file. Then in my Jupyter notebook, I can register my new environment under the name Stochastic-4x4-CustomizedFrozenLake-v0 and I can load it using:. To actually see that the reward was actually modified, I can display env.
P for example. It outputs:. In this article, I presented the OpenAI Gym which is a package that you can use to load various envrionments to test your agent. I detailed some of the most important functions you should know to be ready to train an agent.In this article we will load this environment and we will implement 2 reinforcement learning algorithms: Q-learning and Q-network.
The code associated to this article is available here. There are several ways to solve this tiny game. In this section we will use the Q-learning algorithm. We will then explain the limitations of that model and we will pursue with the use of a neural network to approximate our Q-table.
Q-learning is a a reinforcement learning technique that uses a Q-table to tell the agent what action it should take in each situation.
The figure 1 represents an example of a Q-table for the FrozenLake-v0 environment. According to the previous figure, for each state, the agent will take the action that maximizes its Q value. The question is now: how can I construct such a table? Where does this formula comes from? Do we choose it randomly among all the set of actions possibles? Actually no, there is a better way to choose an action at each step.
This method is called epsilon-greedy and can be summarized as follow:. The goal of the epsilon-greedy strategy is to balance between the exploitation and exploration.
What does that mean? When your agent is in a particular state and it chooses the best action so far based on the Q-value it could get from selecting that action, we say that our agent is exploitating the knowledge of the environment it already acquired. On the contrary, when our agent chooses an action uniformly at random we say that it is exploring the environment. Intuitively it means that our agent will explore its environment more for the few first game plays.
We know how to use the FrozenLake-v0 environment. We know how the Q-learning algorithm works. So we just have to compile all our knowledge so far to come up with the code.
The notebook for this article is available here.The goal of this game is to go from the starting state S to the goal state G by walking only on frozen tiles F and avoid holes H.
0. OpenAI Gym
However, the ice is slippery, so you won't always move in the direction you intend stochastic environment. Came from this Colab and blog Blog. Skip to content. FrozenLake v0 Jump to bottom.
However, the ice is slippery, so you won't always move in the direction you intend stochastic environment Source Came from this Colab and blog Blog Environment Observation Type: Discrete 16 Num Observation State 0 - 15 For 4x4 square, counting each position from left to right, top to bottom Actions Type: Discrete 4 Num Action 0 Move Left 1 Move Down 2 Move Right 3 Move Up Reward Reward is 0 for every step taken, 0 for falling in the hole, 1 for reaching the final goal Starting State Starting state is at the top left corner Episode Termination Reaching the goal or fall into one of the holes Solved Requirements Reaching the goal without falling into hole over consecutive trials.
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For example:. The P attribute will be the most important for your implementation of value iteration and policy iteration. This attribute contains the model for the particular map instance. It is a dictionary of dictionary of lists with the following form:. For example, to get the probability of taking action LEFT in state 0 you would use the following code:. This would return the list: [ 1. There is one tuple in the list, so there is only one possible next state.
The next state will be state 0, according to the second number in the tuple. The final tuple value says that the next state is not terminal. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
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LEFT will print out the number 0. Environment Attributes This class contains the following important attributes: nS :: number of states nA :: number of actions P :: transitions, rewards, terminals The P attribute will be the most important for your implementation of value iteration and policy iteration.
LEFT] This would return the list: [ 1. Running a random policy example. Value Iteration The optimal policies for the different environments is in the.
You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window.We now go one more step further, and add a context to our reinforcement learning problem. Context in this case, means that we have a different optimal action-value function for every state:.
This situation, where we have different states, and actions associated with the states to yield rewards, is called a Markov Decision Process MDP. I found some of his notation unnecessarily verbose, so some may be different. The reason actions is a function of state is we might have different permitted actions per state.
A markov chainunder discrete sigma algebra, is defined as:. This is often called the markov property. What do we do for that?
We can re-formulate our problem using the idea of discounting :. We want to know how good it is to be in a specific stateand how good it is to take an action in a given state.
A policy is a mapping from states to probabilities of selecting an action. We want our state-value function to be maximized at the end. Do you see the recursion here?
How do we solve it? The above is the Bellman optimality equation. I believe the notation in the book is quite verbose, so I will shorten it here for clarity. Recall our previous bellman equation:. Then, we can vectorize our system of equations:. It is the following update:. Now that we have the true value function or approachingwe want to find a better policy. In formal terms:. Why is this better?
We essentially apply that simple idea here. These two are exactly the same, implying that in the condition that equality occurs, we have reached the optimal policy. This is called the policy iteration algorithm. Recall policy iteration. To illustrate how this could work, we took the same situation in frozen lake, a classic MDP problem, and we tried solving it with value iteration.
It only takes a minute to sign up. In reinforcement learning, is a policy always deterministic, or is it a probability distribution over actions from which we sample?
There are multiple questions here: 1. Is a policy always deterministic? If the policy is deterministic then shouldn't the value also be deterministic? What is the expectation over in the value function estimate? Your last question is not very clear "Can a policy lead to routes that have different current values? Can a policy lead to different routes? A policy is a function can be either deterministic or stochastic. It dictates what action to take given a particular state. The value function is not deterministic.
The value of a state is the expected reward if you start at that state and continue to follow a policy. Even if the policy is deterministic the reward function and the environment might not be. Usually, the routes or paths are decomposed into multiple steps, which are used to train value estimators.
This is related to answer 2, the policy can lead to different paths even a deterministic policy because the environment is usually not deterministic. The policy can be stochastic or deterministic.
The expectation is over training examples given the conditions.
The value function is an estimate of the return, which is why it's an expectation. Sign up to join this community. The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered.