tensorforce | Tensorforce: a TensorFlow library for applied reinforcement learning | Reinforcement Learning library

 by   tensorforce Python Version: 0.6.5 License: Apache-2.0

kandi X-RAY | tensorforce Summary

kandi X-RAY | tensorforce Summary

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

Tensorforce is an open-source deep reinforcement learning framework, with an emphasis on modularized flexible library design and straightforward usability for applications in research and practice. Tensorforce is built on top of Google's TensorFlow framework and requires Python 3.

            kandi-support Support

              tensorforce has a medium active ecosystem.
              It has 3230 star(s) with 537 fork(s). There are 146 watchers for this library.
              It had no major release in the last 12 months.
              There are 28 open issues and 630 have been closed. On average issues are closed in 20 days. There are 5 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of tensorforce is 0.6.5

            kandi-Quality Quality

              tensorforce has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              tensorforce 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

              tensorforce 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.
              tensorforce saves you 9559 person hours of effort in developing the same functionality from scratch.
              It has 22460 lines of code, 1379 functions and 165 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed tensorforce and discovered the below as its top functions. This is intended to give you an instant insight into tensorforce implemented functionality, and help decide if they suit your requirements.
            • Runs the network
            • Close the agent
            • Evaluate the agent
            • Handles the action
            • Evaluate the evaluation
            • Return an iterator over the values
            • Create a new tracking module
            • Map a function over the NestedDict
            • Updates the tensorflow
            • Returns signature for given function
            • Compute the state values for each state
            • Calculate parameter value
            • Compute action entropy
            • Apply the policy
            • Step through the input function
            • Compute the policy
            • Decorator for functions
            • Estimates the agent
            • Calculate a single step
            • Perform core act on policy
            • Performs the act on the agent
            • Compute the action values
            • Observe the interaction
            • Enqueues the given state
            • Computes the action function
            • Perform a single step
            Get all kandi verified functions for this library.

            tensorforce Key Features

            No Key Features are available at this moment for tensorforce.

            tensorforce Examples and Code Snippets

            Synopsis with TensorForce
            C++dot img1Lines of Code : 21dot img1no licencesLicense : No License
            copy iconCopy
            from tensorforce.contrib.unreal_engine import UE4Environment
            import random
            if __name__ == "__main__":
                environment = UE4Environment(host="localhost", port=6025, connect=True, discretize_actions=True, num_ticks=6)
            Pre-requisites,Implementation Details,Agent Creation
            Pythondot img2Lines of Code : 16dot img2no licencesLicense : No License
            copy iconCopy
            from tensorforce.agents import VPGAgent
            from tensorforce.agents import DQNAgent
                agent = VPGAgent(states_spec=dict(shape=state_dim, type='float'),
                                     actions_spec=dict(num_actions=action_space, type='int'),
            Create a Custom Agent
            Jupyter Notebookdot img3Lines of Code : 12dot img3License : Permissive (MIT)
            copy iconCopy
            from helper.templates import Agent
            class DoNothingAgent(Agent):
                An agent that chooses NOOP action at every timestep.
                def __init__(self, observation_space, action_space):
                    self.action = [0] * action_space.shape[0]
            copy iconCopy
            from tensorforce.core.layers import Dense
            d = Dense(size=4)
            I've installed cudNN but error "Failed to get convolution algorithm' shows up
            Pythondot img5Lines of Code : 9dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            config = tf.ConfigProto()
            config.gpu_options.allow_growth = True
            config = tf.ConfigProto()
            # config.gpu_options.allow_growth = True

            Community Discussions


            What does the asterisk * mean as the first argument in the signature of a function/class/method?
            Asked 2021-May-29 at 18:49

            I am currently trying to understand the Tensorforce library . I keep stumbling across a signature in the form:



            Answered 2021-May-29 at 02:05

            Any arguments specified after the * "argument" (so in this case, all of them) are keyword-only arguments. They can only be supplied by keyword, rather than positionally; this means your example should be:

            Source https://stackoverflow.com/questions/67746969


            What is the difference between Neural Network Frameworks and RL Algorithm Libraries?
            Asked 2020-Dec-15 at 09:48

            I know this is a silly question, but I cannot find a good way to put it.

            I've worked with TensorFlow and TFAgents, and am now moving to Ray RLlib. Looking at all the RL frameworks/libraries, I got confused about the difference between the two below:

            • frameworks such as Keras, TensorFlow, PyTorch
            • RL implementation libraries such as TFAgents, RLlib, OpenAi Baseline, Tensorforce, KerasRL, etc

            For example, there are Keras codes in TensorFlow and Ray RLlib supports both TensorFlow and PyTorch. How are they all related?

            My understanding so far is that Keras allows to make neural networks and TensorFlow is more of a math library for RL (I don't have enough understanding about PyTorch). And libraries like TFAgents and RLlib use frameworks like Keras and TensorFlow to implement existing RL algorithms so that programmers can utilize them with ease.

            Can someone please explain how they are interconnected/different? Thank you very much.



            Answered 2020-Dec-15 at 09:48

            Yes you are kind of right. Frameworks like Keras, TF (which also uses keras btw) and Pytorch are general Deep Learning frameworks. For most artificial neural network use-cases these frameworks work just fine and your typical pipeline is going to look something like:

            1. Preprocess your dataset
            2. Select an appropriate model for this problem setting
            3. model.fit(dataset)
            4. Analyze results

            Reinforcement Learning though is substantially different from most other Data Science ML applications. To start with, in RL you actually generate your own dataset by having your model (the Agent) interact with an environment; this complicates the situation substantially particularly from a computational standpoint. This is because in the traditional ML scenario most of the computational heavy-lifting is done by that model.fit() call. And the good thing about the aforementioned frameworks is that from that call your code actually enters very efficient C/C++ code (usually also implementing CUDA libraries to use the GPU).

            In RL the big problem is the environment that the agent interacts with. I separate this problem in two parts:

            a) The environment cannot be implemented in these frameworks because it will always change based on what you are doing. As such you have to code the environment and - chances are - it's not gonna be very efficient.

            b) The environment is a key component in the code and it constantly intreacts multiple times with your Agent, and there are multiple ways in which that interaction can be mediated.

            These two factors lead to the necessity to standardize the environment and the interaction between it and the agent. This standardization allows for highly reusable code and also code that is more interpretable by others in how it exactly operates. Furthermore it is possible this way to, for example, easily run parallel environments (TF-agents allows this for example) even though your environment object is not really written to manage this.

            RL frameworks are thus providing this standardization and features that come with it. Their relation to Deep Learning frameworks is that RL libraries often come with a lot of pre-implemented and flexible agent architectures that have been among the most relevant in the literature. These agents are usually nothing more than a some fancy ANN architecture wrapped in some class that standardizes their operation within the given RL framework. Therefore as a backend for these ANN models, RL frameworks use DL frameworks to run the computations efficiently.

            Source https://stackoverflow.com/questions/65291261

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


            No vulnerabilities reported

            Install tensorforce

            A stable version of Tensorforce is periodically updated on PyPI and installed as follows:.


            Please get in touch via mail or on Gitter if you have questions, feedback, ideas for features/collaboration, or if you seek support for applying Tensorforce to your problem. If you want to support the Tensorforce core team (see below), please also consider donating: GitHub Sponsors or Liberapay.
            Find more information at:

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