NEAREST SEQUENCE MEMORY ALGORITHM :
Reinforcement Learning  is a sub-field of Machine Learning where an agent learns by interacting with its environment, observing the results of these interactions and receiving a reward accordingly.

Applying Reinforcement Learning algorithms in the real world is quite challenging. In this project, we applied a reinforcement algorithm called NEAREST SEQUENCE MEMORY on E-PUCK robot in real-world and in simulation world under a controlled environment.

In robotics, the ultimate goal of reinforcement learning is to endow robots with the ability to learn, improve, adapt and reproduce tasks with dynamically changing constraints based on exploration and autonomous learning.

AUTONOMOUS NAVIGATION TURTLEBOT USING GAZEBO:
This video involves simulation of autonomous navigation of turtle robot, in which arbitrary initial and final position can be given to the robot in a known map.Robot analyses the surroundings and decides an obstacle free path autonomously to reach the final point.

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