RL-Agents is a Python-based framework offering ready-to-use PyTorch implementations of key reinforcement learning algorithms. It supports DQN, PPO, A2C, SAC, TD3, and more, enabling researchers and developers to quickly prototype, train, and evaluate agents in various environments with minimal setup.
AI Platforms & Frameworks
#reinforcement learning
#PyTorch-Framework
#RL algorithms
#DQN implementation
#PPO algorithm
#modular agent API
#GPU Acceleration
#OpenAI Gym-Integration
#algorithm customization
#research-grade code
#Performance Benchmarking
#actor-critic methods
#training loops
#logging tools
#visualization support
#industrial experimentation
#Academic Research
#value-based methods
#policy-based methods
#RL prototyping
...
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RL-Agents
RL-Agents is a Python-based framework offering ready-to-use PyTorch implementations of key reinforcement learning algorithms. It supports DQN, PPO, A2C, SAC, TD3, and more, enabling researchers and developers to quickly prototype, train, and evaluate agents in various environments with minimal setup.
AI Platforms & Frameworks
#reinforcement learning
#PyTorch-Framework
#RL algorithms
#DQN implementation
#PPO algorithm
#modular agent API
#GPU Acceleration
#OpenAI Gym-Integration
#algorithm customization
#research-grade code
#Performance Benchmarking
#actor-critic methods
#training loops
#logging tools
#visualization support
#industrial experimentation
#Academic Research
#value-based methods
#policy-based methods
#RL prototyping
...
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What is RL-Agents?
RL-Agents is a research-grade reinforcement learning framework built on PyTorch that bundles popular RL algorithms across value-based, policy-based, and actor-critic methods. The library features a modular agent API, GPU acceleration, seamless integration with OpenAI Gym, and built-in logging and visualization tools. Users can configure hyperparameters, customize training loops, and benchmark performance with a few lines of code, making RL-Agents ideal for academic research, prototyping, and industrial experimentation.
Who will use RL-Agents?
- Reinforcement learning researchers
- Machine learning engineers
- AI developers
- Academics and students
How to use the RL-Agents?
- Step1: Clone the rl-agents repository from GitHub
- Step2: Install dependencies via pip install -r requirements.txt
- Step3: Import the desired agent class and configure hyperparameters
- Step4: Initialize an environment (e.g., OpenAI Gym) and the agent
- Step5: Call agent.train() to start training and agent.evaluate() to test performance
Platform
- mac
- windows
- linux
RL-Agents's Core Features & Benefits
The Core Features
- Implementations of DQN, DDQN, PPO, A2C, SAC, TD3
- Modular, extensible agent API
- GPU acceleration via PyTorch
- Integration with OpenAI Gym environments
- Built-in logging and visualization support
The Benefits
- Speeds up RL prototyping
- Easy algorithm customization
- Research-grade, production-ready code
- Comprehensive coverage of popular RL methods
RL-Agents's Main Use Cases & Applications
- Benchmarking RL algorithms on standard Gym environments
- Developing custom RL solutions for robotics control
- Academic research and algorithm comparison
- Educational tutorials and learning experiments
FAQs of RL-Agents
RL-Agents Company Information
RL-Agents Reviews
5/5
RL-Agents's Main Competitors and alternatives?
- Stable Baselines3
- RLlib
- CleanRL
- Tensorforce
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