RLAgent - Universal Reinforcement Learning Framework

Reinforcement Learning Robotics Automation

Project Overview

RLAgent is a universal reinforcement learning research framework providing a complete toolchain from algorithm research to practical deployment.

Framework Features

๐Ÿงฉ Modular Design

  • Standardized environment interface
  • Pluggable algorithm components
  • Configurable training pipeline

๐ŸŽฎ Rich Algorithm Library

  • DQN / DDPG / SAC
  • PPO / A3C
  • Multi-agent algorithms
  • Model-based RL

๐Ÿ“Š Visualization Tools

  • Real-time training monitoring
  • Policy visualization
  • Performance analysis

๐Ÿš€ Distributed Training

  • Multi-GPU parallel processing
  • Distributed sampling
  • Experience replay management

Supported Environments

  • OpenAI Gym
  • MuJoCo
  • Unity ML-Agents
  • Custom environments

Typical Applications

  1. Robot Control - Robotic arm manipulation, drone flight
  2. Game AI - Strategy games, real-time combat
  3. Resource Optimization - Task scheduling, path planning
  4. Automated Trading - Quantitative strategy optimization

Quick Start

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from rlagent import Agent, Environment

env = Environment('CartPole-v1')
agent = Agent('PPO', env)
agent.train(episodes=1000)

Project Status

โœ… Stable Release - v1.0 Available