DeepRL 🤖

This repository explores Reinforcement Learning (RL) for robotics, with a gradual transition from classical reward-based RL to Deep Reinforcement Learning.

The initial focus is on robotic manipulation in simulation, using MuJoCo, where a Google robotic arm (007) learns to pick up a cube purely through reward and penalty signals, without hard-coded trajectories or heuristics.

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🚀 Current Focus

• RL-based pick-and-place manipulation
• Pure reward–penalty driven learning
• MuJoCo simulation
• Google robot (007) arm
• Limited manipulation task space (for controlled learning)

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🔜 Planned Extensions

• Transition from classical RL → Deep RL
• More complex manipulation tasks
• Navigation + manipulation (moving the cube to different locations)
• Integration with perception (vision-based observations)
• Benchmarking different RL algorithms

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🛠 Tech Stack

• Python
• MuJoCo
• Reinforcement Learning frameworks (to be extended)
• Robotics-focused simulation workflows

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📌 Motivation

This project is part of a long-term exploration into:

• Learning-based control for robots
• Scalability of RL in real-world robotics
• Bridging manipulation and navigation under a unified RL framework