The project aims on building a new type of Artificial intelligence algorithm which is simple and surpasses many already available algorithms for Humanoid or Mu-Jo-Co(Multidimensionla-Joint-with-contact) locomotion related tasks. It simulates a powerful AI Algorithm,called Augmented Random Search (ARS) by training a Half-cheetah (Mu-Jo-Co) to walk and run across a field. to walk and run .

Link to the Google-DeepMind's Video

• Asynchronous Actor-Critic Agents
• Deep Learning
• Deep Reinforcement Learning

• Unlike other AI systems where the exploration occurs after each action (Action Space) , here exploration occurs after end of each episode (Policy space)
• ARS is a shallow learning technique unlike deep learning in other AI's systems (Uses only one perceptron rather than layers of it)
• ARS discards the technique of Gradient Descent for weight adjustment and uses the Method of Finite Differences

• Perceptrons
• Reward Mechanism and updation of weights
• Method of finite Differences to find the best possible direction of movement

• Scaling the update step by standard deviation of Rewards.
• Online normalization of weights.
• Choosing better directions for faster learning.
• Discarding directions that yield lowest rewards.

• Fork and clone the repository using git clone https://github.com/ashutoshtiwari13/Simple-Random-Search.git
• Run pip install -r requirements.txt
• Also check the Simulation.txt for setting up the PyBullet Simulation Environment
• Use the Anaconda Cloud - Spyder IDE (Any framework/IDE of your choice)
• Use Python 3.6 and above
• Run the command python ars.py

Rewards start from being negative as low as -900 and climbs to positive 900 in around 1000 steps.

• Ben Recht's Blog
• Reference paper - Link
• Research paper used - Link

Happy coding 😊 ❤️ ✔️