Search Results for author:
Found 5 papers, 4 papers with code
Discovering Sensorimotor Agency in Cellular Automata using Diversity Search
The research field of Artificial Life studies how life-like phenomena such as autopoiesis, agency, or self-regulation can self-organize in computer simulations.
Evolving Reservoirs for Meta Reinforcement Learning
At the developmental scale, we employ these evolved reservoirs to facilitate the learning of a behavioral policy through Reinforcement Learning (RL).
Emergence of Collective Open-Ended Exploration from Decentralized Meta-Reinforcement Learning
We further find that the agents learned collective exploration strategies extend to an open ended task setting, allowing them to solve task trees of twice the depth compared to the ones seen during training.
Eco-evolutionary Dynamics of Non-episodic Neuroevolution in Large Multi-agent Environments
Neuroevolution (NE) has recently proven a competitive alternative to learning by gradient descent in reinforcement learning tasks.
Flow-Lenia: Towards open-ended evolution in cellular automata through mass conservation and parameter localization
Finally, we show that Flow Lenia enables the integration of the parameters of the CA update rules within the CA dynamics, making them dynamic and localized, allowing for multi-species simulations, with locally coherent update rules that define properties of the emerging creatures, and that can be mixed with neighbouring rules.
