multi-agent q-learning integration tests

Install PettingZoo and its dependencies.

pip install pettingzoo[clasic]

Copy the q-learning script into a Python file (e.g., multi_agent_q_learning.py). Run the script in your Python environment. This script sets up a basic Q-learning framework for three agents in the simple_spread environment from PettingZoo. Each agent has its own Q-table and learns independently based on its experiences and rewards. The agents' actions are decided either randomly (exploration) or by choosing the best-known action from the Q-table (exploitation).

After training your Q-learning model in your chosen environment, use the the graph or grid based A* algorithm to find initial paths. For dynamic adaptation, when the environment changes or when strategic decisions are needed, invoke A* to calculate new paths. If your Q-learning model operates in a similar based environment, the A* algorithm can directly utilize the same graph/grid structure for pathfinding.

Copy the A* algorithm and graph/grid environment code into a Python file. Customize the graph/grid structure as per your environment or application. Run the script to test the A* algorithm's pathfinding in this graph/grid-based environment. This setup is a fundamental example. Depending on your specific application, you may need to customize the structure, the heuristic function, and the way costs are calculated. Integration with the Q-learning model will depend on how the Q-learning agent interacts with its environment and how it can leverage the A* algorithm's pathfinding capabilities.