#PPO

Discover detailed insights into LLM-RLHF-Tuning, implementing multi-stage training including instruction fine-tuning, reward model training, and PPO/DPO algorithms. The project leverages LLaMA and LLaMA2 model capabilities, endorsing efficient, distributed training with frameworks like accelerate and deepspeed. Its flexible configurations enable seamless integration of RM, SFT, Actor, and Critic models. This resource serves as a valuable guide for researchers interested in robust AI model training approaches.

Tensorforce, an open-source library built on TensorFlow, provides a modular architecture for deep reinforcement learning, ideal for research and practical applications. It features a flexible, component-based structure that decouples algorithms from environments, enhancing versatility and user accessibility. Supporting a variety of network architectures, policy distributions, and optimization strategies, Tensorforce facilitates the development of models such as DQN and PPO. The library includes practical example configurations and thorough documentation, although it is important to note that the project is no longer actively maintained.

The project applies the Proximal Policy Optimization (PPO) algorithm to train an AI agent to play Super Mario Bros, completing 31 out of 32 levels. Building on the A3C method, this shows marked performance improvements. It allows training and testing of models with customizable learning rates for optimal results. A Dockerfile facilitates a seamless setup for training and testing, although there may be rendering issues. This framework is ideal for those exploring AI-centered game development and performance optimization.

This project provides a quick and efficient implementation of reinforcement learning algorithms optimized for GPU. Initially based on the NVIDIA Isaac GYM `rl-pytorch`, it currently supports PPO with plans to include more algorithms like SAC and DDPG. Managed by researchers from ETH Zurich and NVIDIA's Robotic Systems Lab, the framework facilitates logging via Tensorboard, Weights & Biases, and Neptune. It is intended for researchers expanding reinforcement learning capabilities and promotes community contributions while following the Google Style Guide for documentation. To set up, clone the repository and adhere to the instructions for seamless integration into various environments.

This project features a modular implementation of deep reinforcement learning algorithms using PyTorch. It seamlessly transitions from simple tasks to complex games, incorporating methods like Double DQN, A2C, and PPO. With efficient data generation and hardware optimization, it's suitable for scalable deep learning research. Support is available for robust testing environments such as Breakout and Mujoco. Discover innovative algorithmic insights and performance metrics visualized through detailed learning curves.

The project delivers a complete pathway for tuning the Vicuna Language Model with LoRA and RLHF methodologies on consumer hardware such as the 2080Ti GPU. It includes comprehensive steps for acquiring Vicuna weights, executing supervised fine-tuning, and incorporating PEFT and reward model adapters. Key phases involve managing CUDA memory and version compatibility challenges, enabling effective model training management. References to FastChat and alpaca-lora provide robust setup support for facilitating advanced machine learning tasks in constrained resource environments.