PettingZoo | standard API for multi-agent reinforcement learning | Reinforcement Learning library

 by   Farama-Foundation Python Version: 1.24.3 License: Non-SPDX

kandi X-RAY | PettingZoo Summary

kandi X-RAY | PettingZoo Summary

PettingZoo is a Python library typically used in Artificial Intelligence, Reinforcement Learning applications. PettingZoo has no bugs, it has no vulnerabilities, it has build file available and it has medium support. However PettingZoo has a Non-SPDX License. You can install using 'pip install PettingZoo' or download it from GitHub, PyPI.

PettingZoo is a Python library for conducting research in multi-agent reinforcement learning, akin to a multi-agent version of Gym. Our website, with comprehensive documentation, is pettingzoo.ml.
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              PettingZoo has a medium active ecosystem.
              It has 1857 star(s) with 302 fork(s). There are 20 watchers for this library.
              There were 4 major release(s) in the last 12 months.
              There are 14 open issues and 279 have been closed. On average issues are closed in 70 days. There are 6 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of PettingZoo is 1.24.3

            kandi-Quality Quality

              PettingZoo has no bugs reported.

            kandi-Security Security

              PettingZoo has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              PettingZoo has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              PettingZoo releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed PettingZoo and discovered the below as its top functions. This is intended to give you an instant insight into PettingZoo implemented functionality, and help decide if they suit your requirements.
            • Gets the bear off of the player .
            • Subroutine for collisions .
            • Get an observation .
            • Generate terrain .
            • Reset the cylinder .
            • Get the vector state of each agent .
            • Moves a piece .
            • Removes agents from the model .
            • Make a world .
            • Plays a move at c .
            Get all kandi verified functions for this library.

            PettingZoo Key Features

            No Key Features are available at this moment for PettingZoo.

            PettingZoo Examples and Code Snippets

            Usage,MultiAgentEnv
            Pythondot img1Lines of Code : 13dot img1no licencesLicense : No License
            copy iconCopy
            from pettingzoo.sisl import pursuit_v4
            
            from coltra.buffers import Action
            from coltra.envs import PettingZooEnv, MultiGymEnv
            
            # env = MultiGymEnv.get_venv(workers=8, env_name="CartPole-v1")  # Creates 8 copies of CartPole
            
            env = PettingZooEnv.get_ven  
            Unity ML-Agents PettingZoo Wrapper-API interface
            C#dot img2Lines of Code : 10dot img2License : Non-SPDX (NOASSERTION)
            copy iconCopy
            from mlagents_envs.environment import UnityEnvironment
            from mlagents_envs.envs import UnityToPettingZooWrapper
            
            unity_env = UnityEnvironment("StrikersVsGoalie")
            env = UnityToPettingZooWrapper(unity_env)
            env.reset()
            for agent in env.agent_iter():
                  
            mlagents_envs.envs.unity_aec_env-UnityAECEnv Objects
            C#dot img3Lines of Code : 5dot img3License : Non-SPDX (NOASSERTION)
            copy iconCopy
            class UnityAECEnv(UnityPettingzooBaseEnv,  AECEnv)
            
             | __init__(env: BaseEnv, seed: Optional[int] = None)
            
             | step(action: Any) -> None
            
             | observe(agent_id)
            
             | last(observe=True)
              

            Community Discussions

            QUESTION

            Keras: AttributeError: 'Adam' object has no attribute '_name'
            Asked 2022-Apr-16 at 15:05

            I want to compile my DQN Agent but I get error: AttributeError: 'Adam' object has no attribute '_name',

            ...

            ANSWER

            Answered 2022-Apr-16 at 15:05

            Your error came from importing Adam with from keras.optimizer_v1 import Adam, You can solve your problem with tf.keras.optimizers.Adam from TensorFlow >= v2 like below:

            (The lr argument is deprecated, it's better to use learning_rate instead.)

            Source https://stackoverflow.com/questions/71894769

            QUESTION

            What are vectorized environments in reinforcement learning?
            Asked 2022-Mar-25 at 10:37

            I'm having a hard time wrapping my head around what and when vectorized environments should be used. If you can provide an example of a use case, that would be great.

            Documentation of vectorized environments in SB3: https://stable-baselines3.readthedocs.io/en/master/guide/vec_envs.html

            ...

            ANSWER

            Answered 2022-Mar-25 at 10:37

            Vectorized Environments are a method for stacking multiple independent environments into a single environment. Instead of executing and training an agent on 1 environment per step, it allows to train the agent on multiple environments per step.

            Usually you also want these environment to have different seeds, in order to gain more diverse experience. This is very useful to speed up training.

            I think they are called "vectorized" since each training step the agent observes multiple states (inserted in a vector), outputs multiple actions (one for each environment), which are inserted in a vector, and receives multiple rewards. Hence the "vectorized" term

            Source https://stackoverflow.com/questions/71549439

            QUESTION

            How does a gradient backpropagates through random samples?
            Asked 2022-Mar-25 at 03:06

            I'm learning about policy gradients and I'm having hard time understanding how does the gradient passes through a random operation. From here: It is not possible to directly backpropagate through random samples. However, there are two main methods for creating surrogate functions that can be backpropagated through.

            They have an example of the score function:

            ...

            ANSWER

            Answered 2021-Nov-30 at 05:48

            It is indeed true that sampling is not a differentiable operation per se. However, there exist two (broad) ways to mitigate this - [1] The REINFORCE way and [2] The reparameterization way. Since your example is related to [1], I will stick my answer to REINFORCE.

            What REINFORCE does is it entirely gets rid of sampling operation in the computation graph. However, the sampling operation remains outside the graph. So, your statement

            .. how does the gradient passes through a random operation ..

            isn't correct. It does not pass through any random operation. Let's see your example

            Source https://stackoverflow.com/questions/70163823

            QUESTION

            Relationship of Horizon and Discount factor in Reinforcement Learning
            Asked 2022-Mar-13 at 17:50

            What is the connection between discount factor gamma and horizon in RL.

            What I have learned so far is that the horizon is the agent`s time to live. Intuitively, agents with finite horizon will choose actions differently than if it has to live forever. In the latter case, the agent will try to maximize all the expected rewards it may get far in the future.

            But the idea of the discount factor is also the same. Are the values of gamma near zero makes the horizon finite?

            ...

            ANSWER

            Answered 2022-Mar-13 at 17:50

            Horizon refers to how many steps into the future the agent cares about the reward it can receive, which is a little different from the agent's time to live. In general, you could potentially define any arbitrary horizon you want as the objective. You could define a 10 step horizon, in which the agent makes a decision that will enable it to maximize the reward it will receive in the next 10 time steps. Or we could choose a 100, or 1000, or n step horizon!

            Usually, the n-step horizon is defined using n = 1 / (1-gamma). Therefore, 10 step horizon will be achieved using gamma = 0.9, while 100 step horizon can be achieved with gamma = 0.99

            Therefore, any value of gamma less than 1 imply that the horizon is finite.

            Source https://stackoverflow.com/questions/71459191

            QUESTION

            OpenAI-Gym and Keras-RL: DQN expects a model that has one dimension for each action
            Asked 2022-Mar-02 at 10:55

            I am trying to set a Deep-Q-Learning agent with a custom environment in OpenAI Gym. I have 4 continuous state variables with individual limits and 3 integer action variables with individual limits.

            Here is the code:

            ...

            ANSWER

            Answered 2021-Dec-23 at 11:19

            As we talked about in the comments, it seems that the Keras-rl library is no longer supported (the last update in the repository was in 2019), so it's possible that everything is inside Keras now. I take a look at Keras documentation and there are no high-level functions to build a reinforcement learning model, but is possible to use lower-level functions to this.

            • Here is an example of how to use Deep Q-Learning with Keras: link

            Another solution may be to downgrade to Tensorflow 1.0 as it seems the compatibility problem occurs due to some changes in version 2.0. I didn't test, but maybe the Keras-rl + Tensorflow 1.0 may work.

            There is also a branch of Keras-rl to support Tensorflow 2.0, the repository is archived, but there is a chance that it will work for you

            Source https://stackoverflow.com/questions/70261352

            QUESTION

            gym package not identifying ten-armed-bandits-v0 env
            Asked 2022-Feb-08 at 08:01

            Environment:

            • Python: 3.9
            • OS: Windows 10

            When I try to create the ten armed bandits environment using the following code the error is thrown not sure of the reason.

            ...

            ANSWER

            Answered 2022-Feb-08 at 08:01

            It could be a problem with your Python version: k-armed-bandits library was made 4 years ago, when Python 3.9 didn't exist. Besides this, the configuration files in the repo indicates that the Python version is 2.7 (not 3.9).

            If you create an environment with Python 2.7 and follow the setup instructions it works correctly on Windows:

            Source https://stackoverflow.com/questions/70858340

            QUESTION

            ValueError: Input 0 of layer "max_pooling2d" is incompatible with the layer: expected ndim=4, found ndim=5. Full shape received: (None, 3, 51, 39, 32)
            Asked 2022-Feb-01 at 07:31

            I have two different problems occurs at the same time.

            I am having dimensionality problems with MaxPooling2d and having same dimensionality problem with DQNAgent.

            The thing is, I can fix them seperately but cannot at the same time.

            First Problem

            I am trying to build a CNN network with several layers. After I build my model, when I try to run it, it gives me an error.

            ...

            ANSWER

            Answered 2022-Feb-01 at 07:31

            Issue is with input_shape. input_shape=input_shape[1:]

            Working sample code

            Source https://stackoverflow.com/questions/70808035

            QUESTION

            Stablebaselines3 logging reward with custom gym
            Asked 2021-Dec-25 at 01:10

            I have this custom callback to log the reward in my custom vectorized environment, but the reward appears in console as always [0] and is not logged in tensorboard at all

            ...

            ANSWER

            Answered 2021-Dec-25 at 01:10

            You need to add [0] as indexing,

            so where you wrote self.logger.record('reward', self.training_env.get_attr('total_reward')) you just need to index with self.logger.record('reward', self.training_env.get_attr ('total_reward')[0])

            Source https://stackoverflow.com/questions/70468394

            QUESTION

            What is the purpose of [np.arange(0, self.batch_size), action] after the neural network?
            Asked 2021-Dec-23 at 11:07

            I followed a PyTorch tutorial to learn reinforcement learning(TRAIN A MARIO-PLAYING RL AGENT) but I am confused about the following code:

            ...

            ANSWER

            Answered 2021-Dec-23 at 11:07

            Essentially, what happens here is that the output of the net is being sliced to get the desired part of the Q table.

            The (somewhat confusing) index of [np.arange(0, self.batch_size), action] indexes each axis. So, for axis with index 1, we pick the item indicated by action. For index 0, we pick all items between 0 and self.batch_size.

            If self.batch_size is the same as the length of dimension 0 of this array, then this slice can be simplified to [:, action] which is probably more familiar to most users.

            Source https://stackoverflow.com/questions/70458347

            QUESTION

            DQN predicts same action value for every state (cart pole)
            Asked 2021-Dec-22 at 15:55

            I'm trying to implement a DQN. As a warm up I want to solve CartPole-v0 with a MLP consisting of two hidden layers along with input and output layers. The input is a 4 element array [cart position, cart velocity, pole angle, pole angular velocity] and output is an action value for each action (left or right). I am not exactly implementing a DQN from the "Playing Atari with DRL" paper (no frame stacking for inputs etc). I also made a few non standard choices like putting done and the target network prediction of action value in the experience replay, but those choices shouldn't affect learning.

            In any case I'm having a lot of trouble getting the thing to work. No matter how long I train the agent it keeps predicting a higher value for one action over another, for example Q(s, Right)> Q(s, Left) for all states s. Below is my learning code, my network definition, and some results I get from training

            ...

            ANSWER

            Answered 2021-Dec-19 at 16:09

            There was nothing wrong with the network definition. It turns out the learning rate was too high and reducing it 0.00025 (as in the original Nature paper introducing the DQN) led to an agent which can solve CartPole-v0.

            That said, the learning algorithm was incorrect. In particular I was using the wrong target action-value predictions. Note the algorithm laid out above does not use the most recent version of the target network to make predictions. This leads to poor results as training progresses because the agent is learning based on stale target data. The way to fix this is to just put (s, a, r, s', done) into the replay memory and then make target predictions using the most up to date version of the target network when sampling a mini batch. See the code below for an updated learning loop.

            Source https://stackoverflow.com/questions/70382999

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install PettingZoo

            PettingZoo includes the following families of environments:. To install the pettingzoo base library, use pip install pettingzoo. This does not include dependencies for all families of environments (there's a massive number, and some can be problematic to install on certain systems). You can install these dependencies for one family like pip install pettingzoo[atari] or use pip install pettingzoo[all] to install all dependencies. We support Python 3.7, 3.8 and 3.9 on Linux and macOS. We will accept PRs related to Windows, but do not officially support it.
            Atari: Multi-player Atari 2600 games (cooperative, competitive and mixed sum)
            Butterfly: Cooperative graphical games developed by us, requiring a high degree of coordination
            Classic: Classical games including card games, board games, etc.
            MAgent: Configurable environments with massive numbers of particle agents, originally from https://github.com/geek-ai/MAgent
            MPE: A set of simple nongraphical communication tasks, originally from https://github.com/openai/multiagent-particle-envs
            SISL: 3 cooperative environments, originally from https://github.com/sisl/MADRL

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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