1,721,099 research outputs found
Enhancing Cartesian genetic programming through preferential selection of larger solutions
No full textWe demonstrate how the efficiency of Cartesian genetic programming methods can be enhanced through the preferential selection of phenotypically larger solutions among equally good solutions. The advantage is demonstrated in two qualitatively different problems: the eight-bit parity problems and the “Paige” regression problem. In both cases, the preferential selection of larger solutions provides an advantage in term of the performance and of speed, i.e. number of evaluations required to evolve optimal or high-quality solutions. Performance can be further enhanced by self-adapting the mutation rate through the one-fifth success rule. Finally, we demonstrate that, for problems like the Paige regression in which neutrality plays a smaller role, performance can be further improved by preferentially selecting larger solutions also among candidates with similar fitness
Automated curriculum learning for embodied agents a neuroevolutionary approach
Full text linkWe demonstrate how the evolutionary training of embodied agents can be extended with a curriculum learning algorithm that automatically selects the environmental conditions in which the evolving agents are evaluated. The environmental conditions are selected to adjust the level of difficulty to the ability level of the current evolving agents, and to challenge the weaknesses of the evolving agents. The method does not require domain knowledge and does not introduce additional hyperparameters. The results collected on two benchmark problems, that require to solve a task in significantly varying environmental conditions, demonstrate that the method proposed outperforms conventional learning methods and generates solutions which are robust to variations and able to cope with different environmental conditions
Qualitative differences between evolutionary strategies and reinforcement learning methods for control of autonomous agents
Full text linkIn this paper we analyze the qualitative differences between evolutionary strategies and reinforcement learning algorithms by focusing on two popular state-of-the-art algorithms: the OpenAI-ES evolutionary strategy and the Proximal Policy Optimization (PPO) reinforcement learning algorithm – the most similar methods of the two families. We analyze how the methods differ with respect to: (i) general efficacy, (ii) ability to cope with rewards which are sparse in time, (iii) propensity/capacity to discover minimal solutions, (iv) dependency on reward shaping, and (v) ability to cope with variations of the environmental conditions. The analysis of the performance and of the behavioral strategies displayed by the agents trained with the two methods on benchmark problems enable us to demonstrate qualitative differences which were not identified in previous studies, to identify the relative weakness of the two methods, and to propose ways to ameliorate some of those weaknesses. We show that the characteristics of the reward function has a strong impact which vary qualitatively not only for the OpenAI-ES evolutionary algorithm and the PPO reinforcement learning algorithm but also for other reinforcement learning algorithms, thus demonstrating the importance of optimizing the characteristic of the reward function to the algorithm used
Reti Neurali e Robot Mobili come modelli delle caratteristiche neuro-biologiche di un organismo.
No full textReti Neurali e Robot Mobili come modelli delle caratteristiche neuro-biologiche di un organismo.
No full textStudying the Emergence of Grounded Representations: Exploring the Power and the Limits of Sensory-Motor Coordination
No full textStudying the Emergence of Grounded Representations: Exploring the Power and the Limits of Sensory-Motor Coordination
No full text
Efficacy of Modern Neuro-Evolutionary Strategies for Continuous Control Optimization
No full textWe analyze the efficacy of modern neuro-evolutionary strategies for continuous control optimization. Overall, the results collected on a wide variety of qualitatively different benchmark problems indicate that these methods are generally effective and scale well with respect to the number of parameters and the complexity of the problem. Moreover, they are relatively robust with respect to the setting of hyper-parameters. The comparison of the most promising methods indicates that the OpenAI-ES algorithm outperforms or equals the other algorithms on all considered problems. Moreover, we demonstrate how the reward functions optimized for reinforcement learning methods are not necessarily effective for evolutionary strategies and vice versa. This finding can lead to reconsideration of the relative efficacy of the two classes of algorithm since it implies that the comparisons performed to date are biased toward one or the other class
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