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Found 5 papers, 1 papers with code
Agent based modelling for continuously varying supply chains
Academic/practical: However, learning in continuously varying environments remains a challenge in the reinforcement learning literature. Methodology: This paper therefore seeks to address whether agents can control varying supply chain problems, transferring learning between environments that require different strategies and avoiding catastrophic forgetting of tasks that have not been seen in a while.
On Temporal References in Emergent Communication
As humans, we use linguistic elements referencing time, such as before or tomorrow, to easily share past experiences and future predictions.
The Effect of Epigenetic Blocking on Dynamic Multi-Objective Optimisation Problems
The mechanism shows improved performance on 12 of the 16 test problems, providing initial evidence that more algorithms should explore the wealth of epigenetic mechanisms seen in the natural world.
Epigenetic opportunities for Evolutionary Computation
The analysis shows that Darwinism and the Modern Synthesis have been incorporated into Evolutionary Computation but that the Extended Evolutionary Synthesis has been broadly ignored beyond:cultural inheritance, incorporated in the sub-set of Swarm Intelligence algorithms, evolvability, through CMA-ES, and multilevel selection, through Multi-Level Selection Genetic Algorithm.
Lifetime policy reuse and the importance of task capacity
A long-standing challenge in artificial intelligence is lifelong reinforcement learning, where learners are given many tasks in sequence and must transfer knowledge between tasks while avoiding catastrophic forgetting.
