From e04808f9ab92601dfb6928f3ae0ca020fe7449d8 Mon Sep 17 00:00:00 2001 From: Benyahia Mohammed Oussama <mohammed.benyahia@etu.ec-lyon.fr> Date: Sun, 2 Mar 2025 16:05:18 +0000 Subject: [PATCH] Edit README.md --- README.md | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 7c6d4e2..6e4b290 100644 --- a/README.md +++ b/README.md @@ -124,10 +124,15 @@ This repository contains my individual work for the **Hands-On Reinforcement Lea --- ## Conclusion -This project successfully implemented and evaluated RL models on **CartPole** and **Panda-Gym** environments using **custom PyTorch implementations and Stable-Baselines3**. The results confirm that: -- **A2C achieves stable and reliable performance**, with high success rates. -- **Tracking with Weights & Biases provides valuable insights** into training dynamics. -- **RL techniques can effectively solve both discrete and continuous control tasks.** + +This project successfully applied reinforcement learning techniques to control both a **CartPole system** and a **Panda-Gym robotic arm** using **REINFORCE** and **A2C** algorithms. The experiments demonstrated that: + +- **REINFORCE** efficiently learned an optimal policy for CartPole but required more episodes to stabilize. +- **A2C (Stable-Baselines3)** improved training stability and efficiency, reaching optimal performance faster. +- **Weights & Biases (W&B)** was valuable for tracking and analyzing training performance in real-time. +- The **Panda-Gym experiment** showed that A2C effectively trained the robotic arm to reach targets in 3D space. + +These results confirm the effectiveness of policy-gradient-based RL methods for solving **control and robotics problems**, highlighting the advantages of **actor-critic approaches** in stabilizing learning. Future work could explore more **advanced RL algorithms** (e.g., PPO, SAC) and extend experiments to **more complex robotic tasks**. Further improvements could include testing **PPO or SAC algorithms** for comparison and expanding experiments to **more complex robotic tasks**. -- GitLab