Navigating the Hex Universe

This project applies reinforcement learning (RL) in the vision of a meta-universe. A robot is placed in the space, which consists of debris in hexagons. Its goal is to avoid traps while reaching the targets. By training a reinforcement learning agent in a grid-based environment using Unity’s ML-Agents toolkit, the agent navigates a grid populated with targets and traps. 

The environment consists of a grid with a specified number of rows and columns. The agent, targets, and traps are placed on this grid. The positions of the targets and traps are randomly generated at the start of each episode to ensure a diverse range of experiences for the agent. The agent is trained using a policy that is updated based on the rewards it receives. The training process involves balancing exploration (trying out new actions) and exploitation (choosing the best-known actions). The agent’s performance is evaluated based on the mean reward over a series of episodes.

The project aims to demonstrate the capabilities of reinforcement learning in game-like environments and explore strategies for reward shaping and policy optimization.

The agent is rewarded based on its actions: The agent receives a reward of +1 for reaching a target. The agent is penalized with a reward of -0.5 for reaching a trap. The agent is penalized with a reward of -0.01 for staying still. The agent receives a reward of +0.01 for moving closer to a target. 
The current reward structure tries to encourage more exploration so the agent don’t stay still to avoid the traps. In multiple rounds of tests, we increased the penalty for staying still and provide a small reward for every step taken towards a target.