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Rl

Rl is a Python library implementing reinforcement-learning algorithms and models, including bandit methods, dynamic programming, Monte Carlo and temporal-difference methods, planning, on-policy control and selected policy-gradient methods. Its model-free solvers accept user-defined states, actions and a transition function that returns the next state, reward and episode termination status.

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Independent OpenViST project profile, based on linked sources.

Rl — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Rl when you want Python implementations to study or demonstrate reinforcement-learning methods, especially in small environments where you can define the state space, actions and transitions yourself.

Who it is for

  • Learners studying reinforcement-learning algorithms.
  • Educators and developers creating small tabular control experiments.
  • Researchers or students demonstrating Monte Carlo evaluation or Monte Carlo Tree Search.

Documented uses

  • Compare implemented approaches such as epsilon-greedy bandits, value iteration, Q-learning and SARSA.
  • Define a small environment through states, actions and a transition function for model-free solvers.
  • Demonstrate ordinary or weighted importance sampling in off-policy Monte Carlo evaluation.
  • Prototype a maze-solving example with Monte Carlo Tree Search.
Before you choose

Requirements & limits

The repository is public and MIT licensed. The README specifies Python 3.8 or later and an editable installation command using uv.

  • Model-free solvers require the user to supply states, actions and a transition function.
  • The method checklist distinguishes implemented techniques from unchecked topics such as ANN, least-squares TD and actor-critic variants.
  • The README characterizes the code as a basic implementation and says it is not production-ready.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install RL in editable mode

Install the repository package in editable mode using the installation command shown in its README.

Before you start

  • Python 3.8 or newer, as indicated by the repository README.
  • The uv command-line tool.

Steps

  1. From the repository directory, run the command below.

    Documented command
    uv pip install -e .

  2. Use the installed package in a Python environment.

Expected outcome

The package is installed in editable mode.

Guide limitations

  • The README provides an installation command but does not document installation verification or detailed environment setup.
  • The repository describes its implementations as basic and not production-ready.
Documentation 1 ↗
Source-based practical guide

Define inputs for a model-free solver

Prepare states, actions, and a transition function in the format described for the library's model-free solvers.

Before you start

  • A collection of states and a collection of actions.
  • A transition function that accepts a state and an action.

Steps

  1. Define states as a sequence of states.

  2. Define actions as a sequence of actions.

  3. Implement a transition function that returns the next state and reward, along with a boolean indicating whether the episode has terminated.

  4. Pass these inputs to a model-free solver.

Expected outcome

The solver inputs follow the interface described in the README; the repository says model-free solvers work with these definitions.

Guide limitations

  • The README does not specify a particular solver call or provide a complete model-free solver example in the documented interface section.
  • The transition-function type annotation in the README contains a spelling inconsistency (transtion).
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
ivanbelenky/RL · Official homepage title · retained in its source language
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