To implement reinforcement learning (RL) for swarm surveillance in your game, you model each drone as an RL agent that learns to select actions to maximize long-term rewards—such as area coverage and bandit detection—while avoiding obstacles and collaborating implicitly with teammates.
Key RL Implementation Concepts
- State (Observation) Each drone's state should encode:
Its location.
The 3x3 sensory field (local map: ground/obstacle/bandit, blue/red flag).
Which cells it has visited recently (optional).
Relative positions of visible bandits and obstacles.
Optionally, a limited communication protocol with teammates.
- Action Space Move up, down, left, right, or stay.
(Optional: "Report bandit" if seen, or communicate.)
- Reward Structure +1 for visiting a previously uncovered ground cell.
+5 for detecting a new bandit.
-1 for attempted collision with obstacle or another drone.
(Optional: +N for achieving >90% coverage fast.)
- Learning Algorithm Start simple: Tabular Q-learning if state space is small, or Deep Q-Network (DQN) for larger/partial observable states.
For swarms, use Multi-Agent RL:
Independent learners: Each drone trains its own Q-table or policy.
Centralized training, decentralized execution: Learn joint policy with global state in training, but only local state at runtime (e.g. MADDPG, MAPPO).
Use frameworks like PettingZoo, RLlib, or even custom gym environments for multi-agent training.
- Integration with Your Simulation Convert your current Pygame grid into a gym-like environment:
Implement reset(), step(action_dict), and render() functions.
Each step lets all drones choose actions (as a dict), environment updates states, calculates rewards, and returns observations/rewards for each.
During training, replace random drone moves with actions sampled from each agent’s evolving policy.
During RL agent evaluation, watch as drones learn to sweep, avoid getting stuck, coordinate, and seek bandits efficiently.