The Axelrod’s model of cultural dissemination is an agent-model designed to investigate the dissemination of culture among interacting agents on a society.

The code contains four experiments for well-being based IMRL reward features.

Interactions of players embedded in a closed square lattice are determined by distance and overall gains and they lead to shifts of reward payoff between temptation and punishment. A new winner balancing against threats is ultimately discovered.

Zombies move toward humans and humans move (faster) away from zombies. They fight if they meet, and humans who lose become zombies.

This model implements a classic scenario used in Reinforcement Learning problem, the “Cliff Walking Problem”. Consider the gridworld shown below (SUTTON; BARTO, 2018). This is a standard undiscounted, episodic task, with start and goal states, and the usual actions causing movement up, down, right, and left. Reward is -1 on all transitions except those into the region marked “The Cliff.” Stepping into this region incurs a reward of -100 and sends the agent instantly back to the start (SUTTON; BARTO, 2018).

The problem is solved in this model using the Q-Learning algorithm. The algorithm is implemented with the support of the NetLogo Q-Learning Extension

This is a Netlogo model which simulates car and bus/tram traffic in Augsburg, specifically between the districts Stadtbergen, Göggingen and the Königsplatz. People either use their cars or public transport to travel to one of their random destinations (Stadtbergen or Göggingen), performing some activity and then returning to their home. Attributes such as travel and waiting time as well as their happiness upon arriving are stored and have an impact on individuals on whether they would consider changing their mode of transport or not.

Developed as a part of a project in the University of Augsburg, Institute of Geography, it simulates the traffic in an intersection or junction which uses either regular traffic lights or traffic lights with a countdown timer. The model tracks the average speed of cars before and after traffic lights as well as the throughput.

MOSAIC is an agent-based NetLogo model of decentralized mission coordination among heterogeneous robots operating under partial observability, limited energy, spatially variable risk, dynamic communication, and individual and cooperative task requirements. Robots discover tasks locally, exchange task information through temporary communication links, submit capability-, energy-, deadline-, and risk-aware bids, compete for individual contracts, and form temporary coalitions for cooperative tasks.

The model integrates decentralized auctions, greedy capability-based coalition formation, contract release and reassignment, four reward regimes, reputation, adaptive bidding strategies, failure traceability, and mission-, network-, information-, inequality-, and coalition-level metrics. It operates without a centralized mission planner or global combinatorial assignment solver.

Seven paired-seed BehaviorSpace experiments comprising 690 official simulation runs evaluate baseline mission viability, reward regimes, communication structure, capability heterogeneity, cooperative-task demand, reputation and adaptive strategies, and mission-incentive strength. The results indicate that structural coordination capacity—particularly information reach, capability compatibility, and feasible coalition construction—has a stronger effect on mission completion than increasing incentive intensity within the tested architecture and parameter ranges.

…

Exploring learned cooperation, coevolution and free-riding. Learning is achieved through Multi-Agent Deep Reinforcement Learning (MADRL) in an ecological environment. The environment emits no other than sparse reproduction rewards. No reward shaping, no explicit cooperation signal.

IDEAL: Agent-Based Model of Residential Land Use Change where the choice of new residential development in based on the Ideal-point decision rule.