8 papers
Qualitative Differences Between Evolutionary Strategies and Reinforcement Learning Methods for Control of Autonomous Agents
Nicola Milano, Stefano Nolfi
In this paper we analyze the qualitative differences between evolutionary strategies and reinforcement learning algorithms by focusing on two popular state-of-the-art algorithms: t…
Automated Curriculum Learning for Embodied Agents: A Neuroevolutionary Approach
Nicola Milano, Stefano Nolfi
We demonstrate how an evolutionary algorithm can be extended with a curriculum learning process that selects automatically the environmental conditions in which the evolving agents…
The Dynamic of Body and Brain Co-Evolution
Paolo Pagliuca, Stefano Nolfi
We introduce a method that permits to co-evolve the body and the control properties of robots. It can be used to adapt the morphological traits of robots with a hand-designed morph…
Efficacy of Modern Neuro-Evolutionary Strategies for Continuous Control Optimization
Paolo Pagliuca, Nicola Milano, Stefano Nolfi
We analyze the efficacy of modern neuro-evolutionary strategies for continuous control optimization. Overall, the results collected on a wide variety of qualitatively different ben…
Long-Term Progress and Behavior Complexification in Competitive Co-Evolution
Luca Simione, Stefano Nolfi
The possibility to use competitive evolutionary algorithms to generate long-term progress is normally prevented by the convergence on limit cycle dynamics in which the evolving age…
Scaling Up Cartesian Genetic Programming through Preferential Selection of Larger Solutions
Nicola Milano, Stefano Nolfi
We demonstrate how efficiency of Cartesian Genetic Programming method can be scaled up through the preferential selection of phenotypically larger solutions, i.e. through the prefe…