objectrl 0.2.1

ObjectRL: An Object-Oriented Reinforcement Learning Codebase

ObjectRL

ObjectRL is a deep reinforcement learning library designed for research and rapid prototyping. It focuses on deep actor-critic algorithms for continuous control tasks such as those in the MuJoCo environment suite, while providing a flexible object-oriented architecture that supports future extensions to value-based and discrete-action methods.

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Features

• Object-oriented design for easy experimentation
• Implements popular deep RL algorithms for continuous control
• Includes experimental implementations of Bayesian and value-based methods
• Supports easy configuration via CLI and YAML files
• Rich examples and tutorials for customization and advanced use cases

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Supported Algorithms

• DDPG (Deep Deterministic Policy Gradient)
• TD3 (Twin Delayed DDPG)
• SAC (Soft Actor-Critic)
• PPO (Proximal Policy Optimization)
• REDQ (Randomized Ensemble Double Q-Learning)
• DRND (Distributional Random Network Distillation)
• OAC (Optimistic Actor-Critic)
• PBAC (PAC-Bayesian Actor-Critic)
• BNN-SAC (Bayesian Neural Network SAC) — experimental, in examples
• DQN (Deep Q-Network) — experimental, in examples

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Installation

Create Environment

conda create -n objectrl python=3.12 -y
conda activate objectrl

Using PyPI

pip install objectrl

From Source

git clone https://github.com/adinlab/objectrl.git
cd objectrl
pip install -e .

Optional Dependencies

To enable additional features such as documentation generation:

pip install objectrl[docs]

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Quick Start Guide

Run your first experiment using Soft Actor-Critic (SAC) on the default cheetah environment:

If installed from PyPI:

python -m objectrl.main --model.name sac

If running from a cloned repo:

python objectrl/main.py --model.name sac

Other examples will assume running from a cloned repo.

Change Algorithm and Environment

Run DDPG on the hopper environment:

python objectrl/main.py --model.name ddpg --env.name hopper

Customize Training Parameters

Train SAC for 100,000 steps and evaluate every 5 episodes:

python objectrl/main.py --model.name sac --env.name hopper --training.max_steps 100000 --training.eval_episodes 5

Use YAML Configuration Files

For more complex or reproducible setups, create YAML config files in objectrl/config/model_yamls/ and specify them at runtime:

python objectrl/main.py --config objectrl/config/model_yamls/ppo.yaml

Example ppo.yaml:

model:
  name: ppo
training:
  warmup_steps: 0
  learn_frequency: 2048
  batch_size: 64
  n_epochs: 10

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Need Help?

If you encounter common issues or errors during installation or usage, please see the Issues guide for solutions and tips.

For other questions or to report bugs, visit our GitHub Issues page.

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Documentation

Explore detailed documentation, tutorials, and API references at: https://objectrl.readthedocs.io

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Citation

If you use ObjectRL in your research, please cite:

@article{baykal2025objectrl,
  title={ObjectRL: An Object-Oriented Reinforcement Learning Codebase}, 
  author={Baykal, Gulcin and  Akg{\"u}l, Abdullah and Haussmann, Manuel and Tasdighi, Bahareh and Werge, Nicklas and Wu Yi-Shan and Kandemir, Melih},
  year={2025},
  journal={arXiv preprint arXiv:2507.03487}
}