GridMind: Multi-Agent Navigation Using Reinforcement Learning in a 3D Environment

Abstract

Multi-agent navigation is a fundamental problem in robotics, autonomous vehicles, warehouse automation, traffic management, and other systems in which several autonomous entities must reach individual destinations while sharing limited space. Classical Multi-Agent Path Finding (MAPF) focuses on producing collision-free paths, whereas Multi-Agent Rein-forcement Learning (MARL) learns policies through interaction and can adapt to changing agent behavior. This literature paper reviews the foundations of reinforcement learning, Q-learning, Deep Q-Networks (DQN), multi-agent reinforcement learning, coordination, collision avoidance, centralized training with decentralized execution (CTDE), and learning-based multi-agent pathfinding. The literature identifies non-stationarity, partial observability, joint action complexity, reward design, communication overhead, scalability, and evaluation consistency as recurring challenges. Based on these findings, GridMind is positioned as a controlled research environment in which multiple agents learn navigation policies in a discrete grid while the same environment is rendered as an interactive 3D simula-tion. A staged approach—random baseline, Q-learning, DQN, independent multi-agent learning, and coordinated learning—provides an understandable and experimentally defensible pathway for studying learned coordination.

Introduction

The text introduces GridMind, a multi-agent navigation system that uses Reinforcement Learning (RL) to help multiple agents navigate a shared grid while avoiding obstacles and collisions. The main objective is to investigate how different learning methods perform in multi-agent environments and whether coordination improves navigation efficiency compared with independent learning.

1. Reinforcement Learning Fundamentals

Reinforcement Learning enables an agent to learn suitable actions by interacting with an environment and receiving rewards or penalties. In GridMind, agents move through a grid using actions such as up, down, left, right, and stay.

The system explores three main approaches:

• Random Policy: Provides a baseline in which agents choose actions randomly.

• Q-Learning: Uses a table of state-action values to learn effective navigation strategies. It is simple and interpretable but becomes less practical as the state space grows.

• Deep Q-Network (DQN): Uses neural networks to estimate action values, allowing agents to handle more complex states.

An epsilon-greedy strategy balances exploration of new actions with exploitation of learned policies.

2. Multi-Agent Reinforcement Learning

Multi-Agent Reinforcement Learning (MARL) extends RL to environments where multiple agents make decisions simultaneously. Each agent must reach its own goal while considering the actions and movements of other agents.

The main challenges include:

• Non-stationarity: Other agents change their policies during learning, making the environment unpredictable.

• State and action complexity: The number of possible joint states and actions increases as the number of agents grows.

• Coordination: Agents must cooperate to avoid blocking each other and improve overall performance.

• Communication: Agents may exchange information to improve cooperation, although communication adds complexity.

Independent learning provides a useful baseline, while coordination methods such as QMIX and MADDPG offer more advanced approaches for cooperative tasks.

3. Multi-Agent Path Finding and Collision Avoidance

Multi-Agent Path Finding (MAPF) focuses on finding collision-free paths for multiple agents moving toward assigned goals. GridMind combines this navigation problem with reinforcement learning.

Potential conflicts include two agents attempting to occupy the same cell, agents swapping positions, entering obstacles, or moving outside the grid. Agents may also experience deadlocks, where they repeatedly block each other without making progress.

Therefore, avoiding collisions alone is insufficient. A successful navigation system must also ensure that agents reach their destinations efficiently.

4. Centralized Training and Decentralized Execution

The text discusses Centralized Training with Decentralized Execution (CTDE) as an advanced MARL approach.

During training, agents can use global information about the environment and other agents. During execution, each agent makes decisions using its own local observations.

Two representative methods are:

• QMIX: Combines individual agent values into a joint action-value to support cooperative decision-making.

• MADDPG: Uses centralized critics during training while maintaining separate policies for individual agents.

These methods can improve coordination, but the text recommends introducing them only after simpler approaches have been evaluated.

5. Proposed Implementation and Technologies

GridMind uses Python for reinforcement-learning logic and Unity for 3D visualization and demonstration. Its purpose is to provide a reproducible environment for comparing navigation and coordination strategies rather than introducing an entirely new learning algorithm.

The proposed development sequence is:

1. Random navigation as a baseline.

2. Q-learning.

3. Single-agent DQN.

4. Independent multi-agent DQN.

5. Advanced coordination methods.

6. Optional CTDE-based extensions.

6. Evaluation Metrics

The system should be evaluated using consistent test scenarios and controlled experiments. Important metrics include:

• Success rate of reaching assigned goals.

• Average cumulative reward.

• Average number of steps and distance travelled.

• Collision count or rate.

• Deadlock rate.

• Episode completion time.

• Scalability as the number of agents increases.

Experiments should use consistent grid layouts, obstacles, starting positions, goals, and random seeds where possible. Results should report average performance and variability rather than relying on a single successful demonstration.

Conclusion

The literature establishes a progression from reinforce-ment learning to deep reinforcement learning and then to multi-agent reinforcement learning. Q-learning provides a foundational value-learning baseline [2], while DQN demon-strates neural value approximation [3]. MARL introduces additional challenges because agents learn simultaneously, creating non-stationarity, joint-action complexity, coordi-nation requirements, and communication or scalability concerns [4], [5]. MAPF provides the complementary navigation perspective in which agents must reach goals without collisions [1], while recent reviews connect deep RL with team-based navigation and emphasize consistent evaluation [9], [10]. CTDE and value-factorization methods such as QMIX provide important directions for cooperative learning [7]. For GridMind, these findings support a controlled grid-based navigation problem presented through a 3D simulation. The proposed research contribution is not a claim of a new MARL algorithm, but a structured investigation of whether learned coordination improves multi-agent navigation relative to independent learning. By combining reproducible environment rules, staged RL baselines, explicit collision and deadlock metrics, controlled experiments, and a 3D visualization layer, the project can provide a technically sound and demonstrable final-year study.

References

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Copyright

Copyright © 2026 Manya D, Mugdha P, Nayana S, Pooja C, Akshay Nayak B S. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.