rl-agents | Reinforcement Learning and Planning algorithms | Reinforcement Learning library
kandi X-RAY | rl-agents Summary
kandi X-RAY | rl-agents Summary
rl-agents is a Python library typically used in Artificial Intelligence, Reinforcement Learning applications. rl-agents has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.
Implementations of Reinforcement Learning and Planning algorithms
Implementations of Reinforcement Learning and Planning algorithms
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Quality
Security
License
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Support
rl-agents has a low active ecosystem.
It has 450 star(s) with 127 fork(s). There are 20 watchers for this library.
It had no major release in the last 6 months.
There are 28 open issues and 54 have been closed. On average issues are closed in 51 days. There are 1 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of rl-agents is current.
Quality
rl-agents has 0 bugs and 0 code smells.
Security
rl-agents has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
rl-agents code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
rl-agents is licensed under the MIT License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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rl-agents releases are not available. You will need to build from source code and install.
Build file is available. You can build the component from source.
Installation instructions, examples and code snippets are available.
rl-agents saves you 2797 person hours of effort in developing the same functionality from scratch.
It has 6647 lines of code, 710 functions and 88 files.
It has medium code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed rl-agents and discovered the below as its top functions. This is intended to give you an instant insight into rl-agents implemented functionality, and help decide if they suit your requirements.
- Evaluate a given experiment
- Close the agent
- Load an environment
- Saves an episode model
- Test the environment
- Compute the comparison between two agents
- Plot the scheduler
- Evaluate an experiment
- Plot node
- Return a dictionary containing the number of visits for each observation
- Returns a random preference policy
- Perform a single step
- Run benchmarks
- Record a transition
- Displays the simulation
- Robustly test the environment
- Backup the state ucb
- Perform a step through the tree
- Perform the forward computation
- Backup the state
- Expand a node to another one
- Plot ellipsoids
- Perform matrix value iteration
- Load a gym environment
- Calculate Bellman residuals
- Plot data from a directory
- Runs an action
- Display an agent
Get all kandi verified functions for this library.
rl-agents Key Features
No Key Features are available at this moment for rl-agents.
rl-agents Examples and Code Snippets
No Code Snippets are available at this moment for rl-agents.
Community Discussions
Trending Discussions on rl-agents
QUESTION
DRQN - Prefix tensor must be either a scalar or vector, but saw tensor
Asked 2017-Jul-25 at 02:51
In following this tutorial, I am receiving the following error:
ValueError: prefix tensor must be either a scalar or vector, but saw tensor: Tensor("Placeholder_2:0", dtype=int32)
The error originates from these lines:
...ANSWER
Answered 2017-Jul-24 at 01:52I met the same problem with the version of tensorflow is 1.2.+.
When i changed it to 1.1.0, the problem resolved.
I think it because the API of rnn_cell.zero_state makes arg batch_size must be a scalar or vector, but not tensor.
So, if you change batch_size to scalar, e.g. 128, the problem also could be resolved.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install rl-agents
You can download it from GitHub.
You can use rl-agents like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
You can use rl-agents like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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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