Vanilla Agents is an open-source PyTorch library offering reference implementations of popular reinforcement learning algorithms such as DQN, DDQN, PPO, and A2C. It includes configurable environment interfaces, logging utilities, model saving, and evaluation scripts to streamline research and development of RL agents.
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Vanilla Agents
Vanilla Agents is an open-source PyTorch library offering reference implementations of popular reinforcement learning algorithms such as DQN, DDQN, PPO, and A2C. It includes configurable environment interfaces, logging utilities, model saving, and evaluation scripts to streamline research and development of RL agents.
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What is Vanilla Agents?
Vanilla Agents is a lightweight PyTorch-based framework that delivers modular and extensible implementations of core reinforcement learning agents. It supports algorithms like DQN, Double DQN, PPO, and A2C, with pluggable environment wrappers compatible with OpenAI Gym. Users can configure hyperparameters, log training metrics, save checkpoints, and visualize learning curves. The codebase is organized for clarity, making it ideal for research prototyping, educational use, and benchmarking new ideas in RL.
Who will use Vanilla Agents?
- RL researchers
- Machine learning students
- AI engineers
- Educational instructors
How to use the Vanilla Agents?
- Step1: Clone the repository from GitHub.
- Step2: Install dependencies via pip (requirements.txt).
- Step3: Choose an algorithm config file (DQN, PPO, A2C).
- Step4: Configure environment and hyperparameters in the config.
- Step5: Run the training script to start learning.
- Step6: Monitor logs and visualize metrics with TensorBoard.
- Step7: Evaluate the trained model using the evaluation script.
Platform
- Linux
- Mac
- Windows
Vanilla Agents's Core Features & Benefits
The Core Features
- DQN and Double DQN implementations
- PPO and A2C policy-gradient agents
- OpenAI Gym environment wrappers
- Configurable hyperparameters
- Logging and TensorBoard support
- Model checkpoint saving and loading
The Benefits
- Easy-to-understand reference code
- Modular design for quick customization
- Ideal for benchmarking and research
- Educational resource for RL concepts
- Lightweight and dependency-minimal
Vanilla Agents's Main Use Cases & Applications
- Benchmarking RL algorithms on standard environments
- Prototyping new reinforcement learning research
- Hands-on educational tutorials for RL courses
- Comparing policy-gradient vs value-based methods
FAQs of Vanilla Agents
Vanilla Agents Company Information
- ranfysvalle02
Vanilla Agents Reviews
5/5
Vanilla Agents's Main Competitors and alternatives?
Stable Baselines3
OpenAI Baselines
RLlib
Dopamine
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