Practical overview
When it fits
Use Adeptrl when a reinforcement-learning experiment can benefit from existing PyTorch baselines and modular training components, especially when moving between a local single-GPU workflow and documented multi-GPU options.
Who it is for
- Researchers building or comparing reinforcement-learning agents.
- Python developers adapting networks, agents, or environments for training experiments.
- Teams planning local or multi-GPU training workflows with PyTorch.
Documented uses
- Train an agent with a baseline A2C or IMPALA workflow.
- Integrate custom agents, networks, submodules, and environments.
- Record training runs, save or reload models, evaluate them, and render environments.
Requirements & limits
The public GitHub repository identifies the project as GPL-3.0 licensed.
- The README describes the code as early access and says interfaces may change.
- Distributed A2C requires NCCL; the documented IMPALA actor-learner mode requires Ray and is resource-intensive.
- The README describes IMPALA as single-node and says it does not support multiple nodes.
- InfiniBand is listed as supported for distributed communication, but the README says it was untested in the project’s setup.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install Adeptrl from its GitHub repository
Clone the project and install its package in editable mode with the documented optional dependencies.
Before you start
- Git
- Python and pip
Steps
Clone https://github.com/heronsystems/adeptRL.
Change into the adeptRL directory.
Run the command below.
pip install -e .[all]
Expected outcome
The repository package is installed in editable mode with the `all` extras requested.
Guide limitations
- The README labels the code early access and warns that interfaces may change.
- This installation procedure does not cover running a training job.
Start a local A2C training run
Launch the documented local-mode A2C example using the BeamRider environment identifier.
Before you start
- Adeptrl installed
- A compatible environment identified as
BeamRiderNoFrameskip-v4 - The README recommends 4 GB or more of GPU memory, 8 GB or more of RAM, and four or more CPU cores.
Steps
From the project environment, run the command below.
python -m adept.app local --env BeamRiderNoFrameskip-v4Check the default log directory at
/tmp/adept_logs/for the TensorBoard file, saved models, and other metadata.
Expected outcome
The documented local A2C training command is launched, with run logs and model artifacts directed to the default log directory.
Guide limitations
- The README gives resource recommendations, not a guarantee that the example will run on a particular system.
- The project describes itself as early access, with interfaces subject to change.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 17 October 2026
- Original website title
- heronsystems/adeptRL · Official homepage title · retained in its source language