Practical overview
When it fits
Use it when you want code examples to study or adapt for deep reinforcement-learning experiments in Gym environments, and can work with the repository's documented Python and library requirements.
Who it is for
- Learners studying how deep reinforcement-learning algorithms are implemented.
- Developers exploring algorithm examples as starting points for Gym experiments.
- Educators preparing demonstrations of reinforcement-learning code and behavior.
Documented uses
- Compare implementations of DQN, policy-gradient, actor-critic, PPO, SAC, DDPG and TD3 approaches.
- Inspect example tasks such as CartPole, MountainCar, Pendulum and BipedalWalker.
- Adapt example code for learning-focused reinforcement-learning experiments.
Requirements & limits
The public GitHub repository specifies an MIT license.
- The README specifies Python 3.6 or earlier, PyTorch 0.4 or later, Gym 0.10 or later, and TensorBoardX.
- OpenAI Baselines is listed as an optional dependency.
- The README states that development may include breaking changes.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install the documented dependencies
Set up the Python packages listed by the repository before working with its examples.
Before you start
- Python 3.6 or earlier
- PyTorch 0.4 or later
- Gym 0.10 or later
- TensorBoardX
Steps
Run the command below from the repository directory.
pip install -r requirements.txtIf Gym needs separate installation, run the command below.
pip install gymInstall PyTorch using the official PyTorch website instructions.
For TensorBoardX, the README lists the command below.
pip install tensorboardX
Expected outcome
The documented Python dependencies are installed for working with the repository's examples.
Guide limitations
- The README's version requirements are dated and specify Python 3.6 or earlier.
- The installation instructions do not establish compatibility with current package versions.
Run the documented TD3 BipedalWalker test
Start the repository's named TD3 BipedalWalker test mode after setting up its dependencies.
Before you start
- The repository and its documented Python dependencies installed
- The TD3 example under
Char10 TD3
Steps
Change to the
Char10 TD3directory.Run the command below.
python TD3_BipedalWalker-v2.py --mode test
Expected outcome
The README describes this command as showing a BipedalWalker test.
Guide limitations
- The README does not specify current Gym environment availability or compatibility.
- The repository warns that breaking changes may occur during development.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 16 October 2026
- Original website title
- sweetice/Deep-reinforcement-learning-with-pytorch · Official homepage title · retained in its source language