Discrete action space openai gym

By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I am implementing a gym environment and I have several input arrays as my input different sizes.

The most simple method to integrate my environment into the gym is to use the Dict space as my environment space which each of the entries denote one of the spaces but the problem is that the stable-baseline library which I am aiming to use for my training does not accept the Dict space type?

Is there another library that accepts Dicts spaces as its input? Learn more. How to integrate Dict space of OpenAI gym into a reinforcement learning framework? Ask Question. Asked 7 days ago. Active 7 days ago. Viewed 8 times. Saeid Ghafouri Saeid Ghafouri 9 1 1 bronze badge. Active Oldest Votes. Sign up or log in Sign up using Google. Sign up using Facebook.

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discrete action space openai gym

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discrete action space openai gym

Stack Overflow works best with JavaScript enabled.If you are a beginner in reinforcement learning and want to implement it, then OpenAIGym is the right place to begin from. Reinforcement learning is an interesting area of Machine learning. The rough idea is that you have an agent and an environment. The agent takes actions and environment gives reward based on those actions, The goal is to teach the agent optimal behaviour in order to maximize the reward received by the environment.

For example, have a look at the diagram. This maze represents our environment. Our purpose would be to teach the agent an optimal policy so that it can solve this maze.

The maze will provide a reward to the agent based on the goodness of each action it takes. Also, each action taken by agent leads it to the new state in the environment. OpenAI Gym provides really cool environments to play with. These environments are divided into 7 categories. One of the categories is Classic Control which contains 5 environments. I will be solving 3 environments. I will leave 2 environments for you to solve as an exercise. Please read this doc to know how to use Gym environments.

A pole is attached by an un-actuated joint to a cart, which moves along a frictionless track. The pendulum starts upright, and the goal is to prevent it from falling over.

The episode ends when the pole is more than 15 degrees from vertical, or the cart moves more than 2. In this environment, we have a discrete action space and continuous state space. In order to maximize the reward agent has to balance the pole as long as it can.

Deep Q-Network and OpenAI Gym

I am solving this problem with the DQN algorithm, which is compatible and works well when you have a discrete action space and continuous state space. I will not be going into details of how DQN works. DQN approximate the actions using a neural network. There is much more to read about it. There are pretty good resources on the DQN online. I have included the link of these resources at the end of this blog. Network Architecture for Cart-Pole.

I have attached the snippet of my DQN algorithm which shows network architecture and hyperparameters I have used. Input size of the network should be equal to the number of states. Output size of the network should be equal to the number of actions an agent can take.

If there are 2 possible actions then the network will output 2 scores. These 2 scores correspond to 2 actions and we select the action which has the highest score. My network size is small. It consists of 2 hidden layers of size 24 each with relu activation.Teach a Taxi to pick up and drop off passengers at the right locations with Reinforcement Learning. Most of you have probably heard of AI learning to play computer games on their own, a very popular example being Deepmind.

Deepmind hit the news when their AlphaGo program defeated the South Korean Go world champion in There had been many successful attempts in the past to develop agents with the intent of playing Atari games like Breakout, Pong, and Space Invaders. Each of these programs follow a paradigm of Machine Learning known as Reinforcement Learning. If you've never been exposed to reinforcement learning before, the following is a very straightforward analogy for how it works. Consider the scenario of teaching a dog new tricks.

The dog doesn't understand our language, so we can't tell him what to do. Instead, we follow a different strategy. We emulate a situation or a cueand the dog tries to respond in many different ways. If the dog's response is the desired one, we reward them with snacks. 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.

That's like learning "what to do" from positive experiences. Similarly, dogs will tend to learn what not to do when face with negative experiences. 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. 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.

Introduction: Reinforcement Learning with OpenAI Gym

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.It uses off-policy data and the Bellman equation to learn the Q-function, and uses the Q-function to learn the policy.

This approach is closely connected to Q-learning, and is motivated the same way: if you know the optimal action-value functionthen in any given state, the optimal action can be found by solving. DDPG interleaves learning an approximator to with learning an approximator toand it does so in a way which is specifically adapted for environments with continuous action spaces. But what does it mean that DDPG is adapted specifically for environments with continuous action spaces?

It relates to how we compute the max over actions in. When there are a finite number of discrete actions, the max poses no problem, because we can just compute the Q-values for each action separately and directly compare them. This also immediately gives us the action which maximizes the Q-value. Using a normal optimization algorithm would make calculating a painfully expensive subroutine.

And since it would need to be run every time the agent wants to take an action in the environment, this is unacceptable. Because the action space is continuous, the function is presumed to be differentiable with respect to the action argument.

This allows us to set up an efficient, gradient-based learning rule for a policy which exploits that fact. Then, instead of running an expensive optimization subroutine each time we wish to computewe can approximate it with. See the Key Equations section details. This Bellman equation is the starting point for learning an approximator to. Suppose the approximator is a neural networkwith parametersand that we have collected a set of transitions where indicates whether state is terminal.

We can set up a mean-squared Bellman error MSBE function, which tells us roughly how closely comes to satisfying the Bellman equation:. This choice of notation corresponds to what we later implement in code. There are two main tricks employed by all of them which are worth describing, and then a specific detail for DDPG. Trick One: Replay Buffers.

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All standard algorithms for training a deep neural network to approximate make use of an experience replay buffer. This is the set of previous experiences. In order for the algorithm to have stable behavior, the replay buffer should be large enough to contain a wide range of experiences, but it may not always be good to keep everything.

If you only use the very-most recent data, you will overfit to that and things will break; if you use too much experience, you may slow down your learning. This may take some tuning to get right.

Observe that the replay buffer should contain old experiences, even though they might have been obtained using an outdated policy. Why are we able to use these at all?By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

Artificial Intelligence Stack Exchange is a question and answer site for people interested in conceptual questions about life and challenges in a world where "cognitive" functions can be mimicked in purely digital environment.

It only takes a minute to sign up. Printing actionspace for Pong-v0 gives 'Discrete 6 ' as output, i.

discrete action space openai gym

Why this discrepency? Further is that necessary to identify which number from 0 to 5 corresponds to which action in gym environment? You can try the actions yourselves, but if you want another reference, check out the documentation for ALE at GitHub. In particular, 0 means no action, 1 means fire, which is why they don't have an effect on the racket. The interesting part is, when I run the script above for the same action from 2 to 5 two times, I have different results.

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Sometimes the racket reaches the top bottom border, and sometimes it doesn't. I think there might be some randomness on the speed of the racket, so it might be hard to measure which type of UP 2 or 4 is faster. The inconsistency mentioned by Icyblade is due to the mechanics of the Pong environment.

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The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered. What are different actions in action space of environment of 'Pong-v0' game from openai gym?

Ask Question. Asked 3 years, 4 months ago. Active 2 years, 8 months ago. Viewed 11k times. Some games need all of them some not. It depends on your agent, whether it can figure out which control is for which action or not. Active Oldest Votes. Here's a better way: env. ComputerScientist ComputerScientist 1 1 silver badge 4 4 bronze badges. Icyblade Icyblade 3 3 bronze badges. Sign up or log in Sign up using Google. Sign up using Facebook.Those interested in the world of machine learning are aware of the capabilities of reinforcement-learning-based AI.

The past few years have seen many breakthroughs using reinforcement learning RL. The company DeepMind combined deep learning with reinforcement learning to achieve above-human results on a multitude of Atari games and, in Marchdefeated Go champion Le Sedol four games to one.

Though RL is currently excelling in many game environments, it is a novel way to solve problems that require optimal decisions and efficiency, and will surely play a part in machine intelligence to come. Join the O'Reilly online learning platform.

Get a free trial today and find answers on the fly, or master something new and useful. Gym is written in Python, and there are multiple environments such as robot simulations or Atari games. There is also an online leaderboard for people to compare results and code.

Reinforcement learning, explained simply, is a computational approach where an agent interacts with an environment by taking actions in which it tries to maximize an accumulated reward. Here is a simple graph, which I will be referring to often:. Given the updated state and reward, the agent chooses the next action, and the loop repeats until an environment is solved or terminated. First, we need an environment.

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For our first example, we will load the very basic taxi environment. You will notice that resetting the environment will return an integer. This number will be our initial state.

All possible states in this environment are represented by an integer ranging from 0 to We can determine the total number of possible states using the following command:. The taxi will turn green when it has a passenger aboard. While we see colors and shapes that represent the environment, the algorithm does not think like us and only understands a flattened state, in this case an integer. This shows us there are a total of six actions available.

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Gym will not always tell you what these actions mean, but in this case, the six possible actions are: down 0up 1right 2left 3pick-up 4and drop-off 5. Every Gym environment will return these same four variables after an action is taken, as they are the core variables of a reinforcement learning problem. Take a look at the rendered environment. What do you expect the environment would return if you were to move left? It would, of course, give the exact same return as before.

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The environment always gives a -1 reward for each step in order for the agent to try and find the quickest solution possible. If you were measuring your total accumulated reward, constantly running into a wall would heavily penalize your final reward. The environment will also give a reward every time you incorrectly pick up or drop off a passenger. One surprising way you could solve this environment is to choose randomly among the six possible actions.A key feature of SAC, and a major difference with common RL algorithms, is that it is trained to maximize a trade-off between expected return and entropy, a measure of randomness in the policy.

In our implementation, we use an entropy coefficient as in OpenAI Spinning or Facebook Horizonwhich is the equivalent to the inverse of reward scale in the original SAC paper. The main reason is that it avoids having too high errors when updating the Q functions.

discrete action space openai gym

However if actions is not Nonethis function will return the probability that the given actions are taken with the given parameters observation, state, … on this model. For discrete action spaces, it returns the probability mass; for continuous action spaces, the probability density.

Return the VecNormalize wrapper of the training env if it exists. If False, only variables included in the dictionary will be updated. As such training after using this function may lead to less-than-optimal results.

Checks the validity of the environment, and if it is coherent, set it as the current environment. The initial state of the policy. For feedforward policies, None. For a recurrent policy, a NumPy array of shape self. Policy object that implements actor critic, using a MLP 2 layers of 64with layer normalisation. Similarly to the example given in the examples page.

You can easily define a custom architecture for the policy network:. Stable Baselines master. Can I use? Equivalent to inverse of reward scale in the original SAC paper. Cf DDPG for the different action noise type. If None defaultuse random seed. Depending on the action space the output is: Discrete: probability for each possible action Box: mean and standard deviation of the action output However if actions is not Nonethis function will return the probability that the given actions are taken with the given parameters observation, state, … on this model.

Must have the same number of actions and observations. This has no effect if actions is None. Returns: np. It takes the local and global variables. If it returns False, training is aborted.