RxAgent-Zoo is a Python-based library that leverages RxPY reactive extensions to implement and orchestrate reinforcement learning algorithms. It provides pre-built agent templates, including DQN, PPO, and A2C, enabling researchers and developers to quickly configure experiments, manage data streams, and monitor training progress across multiple Gym environments. Its modular design simplifies prototyping and accelerates development of custom RL solutions.
AI Platforms & Frameworks
#reactive reinforcement learning
#data stream management
#asynchronous training
#parallel environments
#RL benchmarks
#DQN agent
#PPO agent
#A2C learning
#DDPG algorithm
#logging and monitoring
#modular architecture
#Scalable Training
#experiment reproducibility
#data preprocessing
#Machine Learning Framework
#Gym integration
#Atari games
#reactive data handling
#research acceleration
#AI experimentation
...
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RxAgent-Zoo
RxAgent-Zoo is a Python-based library that leverages RxPY reactive extensions to implement and orchestrate reinforcement learning algorithms. It provides pre-built agent templates, including DQN, PPO, and A2C, enabling researchers and developers to quickly configure experiments, manage data streams, and monitor training progress across multiple Gym environments. Its modular design simplifies prototyping and accelerates development of custom RL solutions.
AI Platforms & Frameworks
#reactive reinforcement learning
#data stream management
#asynchronous training
#parallel environments
#RL benchmarks
#DQN agent
#PPO agent
#A2C learning
#DDPG algorithm
#logging and monitoring
#modular architecture
#Scalable Training
#experiment reproducibility
#data preprocessing
#Machine Learning Framework
#Gym integration
#Atari games
#reactive data handling
#research acceleration
#AI experimentation
...
Featured
What is RxAgent-Zoo?
At its core, RxAgent-Zoo is a reactive RL framework that treats data events from environments, replay buffers, and training loops as observable streams. Users can chain operators to preprocess observations, update networks, and log metrics asynchronously. The library offers parallel environment support, configurable schedulers, and integration with popular Gym and Atari benchmarks. A plug-and-play API allows seamless swapping of agent components, facilitating reproducible research, rapid experimentation, and scalable training workflows.
Who will use RxAgent-Zoo?
- RL researchers
- Machine learning engineers
- Data scientists
- AI educators
How to use the RxAgent-Zoo?
- Step1: Clone the RxAgent-Zoo repository from GitHub
- Step2: Install dependencies via pip install -r requirements.txt
- Step3: Choose a Gym environment and configure the agent in the config file
- Step4: Run the training script to start the reactive RL pipeline
- Step5: Monitor real-time metrics and adjust hyperparameters as needed
- Step6: Extend or customize agent streams for new algorithms
Platform
- mac
- windows
- linux
RxAgent-Zoo's Core Features & Benefits
The Core Features
- Reactive RL pipelines with RxPY
- Pre-implemented agents: DQN, PPO, A2C, DDPG
- Parallel environment execution
- Asynchronous data stream management
- Built-in logging and monitoring
The Benefits
- Accelerates RL experiment setup
- Modular and extensible architecture
- Scalable across environments
- Improved reproducibility
- Flexible reactive data handling
RxAgent-Zoo's Main Use Cases & Applications
- Benchmarking RL algorithms on Gym and Atari environments
- Prototyping custom reinforcement learning methods
- Teaching RL concepts in academia
- Conducting multi-agent and parallel training experiments
FAQs of RxAgent-Zoo
RxAgent-Zoo Company Information
RxAgent-Zoo Reviews
5/5
RxAgent-Zoo's Main Competitors and alternatives?
- Stable Baselines3
- RLlib
- Dopamine
- Tensorforce
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