Altruism in Fuzzy Reinforcement Learning
Bibliographic Data
| ID | 22107484 |
|---|---|
| Authors | Rachel M Haighton (0009-0004-2544-5943, Carleton University), Howard M Schwartz (0000-0002-4489-5892, Carleton University), Sidney N Givigi (0000-0002-3829-3545, Queen's University) |
| Year | 2025 |
| Volume | 12 |
| Issue | 1 |
| Pages | 348-361 |
| Publication date | 2025-02-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2024.3460653 |
| OpenAlex | W4403391214 |
| Language | EN |
| References cited | 12 |
We propose using a genetic algorithm to select hyperparameters in multiagent reinforcement learning (MARL) settings. In particular, we look at this in the context of cooperation and altruism. We show through the use of three continuous space games, that certain algorithmic hyperparameters are better suited to allow to agents learn altruistic behaviors. The agents learn using fuzzy actor critic learning algorithms in either a hierarchical structure or a single actor critic policy. The genetic algorithm selects the discount factors, the reward weights, and the standard deviation of noise applied to actor during learning. The genetic algorithm uses a fitness function based on the ratio of successful tests the group of agents can pass after training. This automated selection of these specific hyperparameters show that they are important for cooperation and also not trivial to select
Fuzzy logic · Machine learning · Reinforcement · Reinforcement learning · Computer Science · Experimental Behavioral Economics Studies · Psychology · Social Psychology · Artificial Intelligence
| Citation velocity | historical |
|---|---|
| Highly cited | No |