slimevolleygym | simple OpenAI Gym environment | Reinforcement Learning library

 by   hardmaru Python Version: 0.1.0 License: Apache-2.0

kandi X-RAY | slimevolleygym Summary

kandi X-RAY | slimevolleygym Summary

slimevolleygym is a Python library typically used in Artificial Intelligence, Reinforcement Learning, Deep Learning, Tensorflow applications. slimevolleygym has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install slimevolleygym' or download it from GitHub, PyPI.

A simple OpenAI Gym environment for single and multi-agent reinforcement learning
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            kandi-support Support

              slimevolleygym has a low active ecosystem.
              It has 612 star(s) with 89 fork(s). There are 11 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 1 open issues and 10 have been closed. On average issues are closed in 43 days. There are 1 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of slimevolleygym is 0.1.0

            kandi-Quality Quality

              slimevolleygym has 0 bugs and 0 code smells.

            kandi-Security Security

              slimevolleygym has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              slimevolleygym code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              slimevolleygym is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              slimevolleygym releases are not available. You will need to build from source code and install.
              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.
              slimevolleygym saves you 736 person hours of effort in developing the same functionality from scratch.
              It has 1697 lines of code, 120 functions and 15 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed slimevolleygym and discovered the below as its top functions. This is intended to give you an instant insight into slimevolleygym implemented functionality, and help decide if they suit your requirements.
            • Step the agent
            • Set action
            • Convert a discrete action to a box
            • Get an observation
            • Perform a single step
            • Update the view
            • Update agent state
            • Get the status of the agent
            • Display the ball
            • Draw a circle
            • Draw a half circle
            • Add attributes to geom
            • Train the model
            • Make an environment for a given seed
            • Seed random seeds
            • Generate the reward of a policy
            • Render an ANTsImage
            • Rollout a policy
            • Perform a step
            • Render an array of ANTs
            • Set model parameters
            • Set the night color
            • Create an environment for the given seed
            • Evaluate a policy
            • Generate a rollout
            • Seed random seed
            • Reset the environment
            • Predict the state of an observation
            • Create a SlimePolicyLite from a file
            • Make SlimePolicy from filename
            • Set model parameters
            • Forward an action
            Get all kandi verified functions for this library.

            slimevolleygym Key Features

            No Key Features are available at this moment for slimevolleygym.

            slimevolleygym Examples and Code Snippets

            No Code Snippets are available at this moment for slimevolleygym.

            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 slimevolleygym

            Install from pip package, if you only want to use the gym environment, but don't want the example usage scripts:.

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            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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