cubrink/mujoco-2.1-rl-project
Implementing Deep Reinforcement Learning Algorithms in Python for use in the MuJoCo Physics Simulator
This project helps researchers and students explore and apply cutting-edge reinforcement learning techniques to simulate complex physical behaviors. By providing pre-built algorithms, it allows you to input defined simulation environments, like a robotic arm or bipedal robot, and output trained policies that can control these systems to perform tasks. It's designed for machine learning practitioners and robotics engineers working with simulated physical systems.
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Use this if you need to quickly implement and experiment with various deep reinforcement learning algorithms within the MuJoCo physics simulator.
Not ideal if you are looking for a plug-and-play solution for real-world robotics hardware or if you are unfamiliar with Python and simulation environments.
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Dec 18, 2021
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