Practical overview
When it fits
Use it when your reinforcement-learning work is in Julia and you need components for implementing algorithms or a composable workflow for experimentation and agent evaluation.
Who it is for
- Julia developers implementing reinforcement-learning algorithms
- Researchers and practitioners who want to benchmark or compare algorithms
- Users evaluating and diagnosing agents across tabular or deep reinforcement-learning work
Documented uses
- Implement an algorithm using reusable components and interfaces.
- Assemble an experiment from a policy, environment, stopping condition and measurement hook.
- Run benchmark experiments and compare algorithms.
- Collect episode rewards and evaluate or diagnose an agent.
Requirements & limits
- The package is Julia-based, so using it requires a Julia environment.
- The basic example uses CartPole and a random policy; it illustrates the experiment structure rather than demonstrating learning or benchmark results.
- The top-level package wraps subpackages for base interfaces, environments, core functionality and an algorithm zoo.
- The README links to tutorials for component assembly and blog posts for implementation discussion.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install the package and run a CartPole example
Install ReinforcementLearning.jl in Julia and run a 1,000-step experiment with a random policy, CartPole environment, and episode-reward collection.
Before you start
- A Julia REPL.
Steps
In the Julia REPL, enter package mode with
]and add the package usingadd ReinforcementLearning.Return to the Julia prompt and enter
using ReinforcementLearning.Run
run(RandomPolicy(), CartPoleEnv(), StopAfterNSteps(1_000), TotalRewardPerEpisode()).
Expected outcome
The example runs a random policy in CartPole for up to 1,000 steps and collects total reward per episode.
Guide limitations
- The example uses a random policy; it does not demonstrate training a learning algorithm.
- The source describes the stopping condition as 1,000 steps and the hook as collecting episode rewards; it does not specify a particular reward value or test result.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 18 October 2026
- Original website title
- JuliaReinforcementLearning/ReinforcementLearning.jl · Official homepage title · retained in its source language