15,188 research outputs found
Novelty Search in Competitive Coevolution
One of the main motivations for the use of competitive coevolution systems is
their ability to capitalise on arms races between competing species to evolve
increasingly sophisticated solutions. Such arms races can, however, be hard to
sustain, and it has been shown that the competing species often converge
prematurely to certain classes of behaviours. In this paper, we investigate if
and how novelty search, an evolutionary technique driven by behavioural
novelty, can overcome convergence in coevolution. We propose three methods for
applying novelty search to coevolutionary systems with two species: (i) score
both populations according to behavioural novelty; (ii) score one population
according to novelty, and the other according to fitness; and (iii) score both
populations with a combination of novelty and fitness. We evaluate the methods
in a predator-prey pursuit task. Our results show that novelty-based approaches
can evolve a significantly more diverse set of solutions, when compared to
traditional fitness-based coevolution.Comment: To appear in 13th International Conference on Parallel Problem
Solving from Nature (PPSN 2014
The Organizational Fitness Navigator: Creating and Measuring Organizational Fitness for Fast-Paced Transformation
In the fast-changing environment of today dynamic capabilities to manage organizational transformation are regarded as crucial for business survival and improved performance. Although dynamic organizational capabilities have been receiving intense scrutiny by researchers and practitioners in the past few years, relatively little attention has been directed towards creating a systemic model of dynamic capabilities, and how to effectively measure what the authors call organizational fitness capabilities. This paper builds on the concepts of organizational fitness and its profiling (OFP), and proposes the organizational fitness navigator (OFN) as a systemic model of dynamic organizational capabilities. Part of the OFP model is a systemic scorecard (SCC) as a measurement tool for organizational fitness - in contrast to the well-known balanced scorecard (BSC) - for improving business survival and performance in increasingly networked environments.dynamic capabilities, organizational fitness, organizational fitness profiling, organizational fitness navigator, systemic scorecard
Evolving recurrent neural network controllers by incremental fitness shaping
Time varying artificial neural networks are commonly used for dynamic problems such as games controllers and robotics as they give the controller a memory of what occurred in previous states which is important as actions in previous states can influence the final success of the agent. Because of this temporal dependence, methods such as back-propagation can be difficult to use to optimise network parameters and so genetic algorithms (GAs) are often used instead. While recurrent neural networks (RNNs) are a common network used with GAs, long short term memory (LSTM) networks have had less attention. Since, LSTM networks have a wide range of temporal dynamics, in this paper, we evolve an LSTM network as a controller for a lunar lander task with two evolutionary algorithms: a steady state GA (SSGA) and an evolutionary strategy (ES). Due to the presence of a large local optima in the fitness space, we implemented an incremental fitness scheme to both evolutionary algorithms. We also compare the behaviour and evolutionary progress of the LSTM with the behaviour of an RNN evolved via NEAT and ES with the same fitness function. LSTMs proved themselves to be evolvable on such tasks, though the SSGA solution was outperformed by the RNN. However, despite using an incremental scheme, the ES developed solutions far better than both showing that ES can be used both for incremental fitness and for LSTMs and RNNs on dynamic tasks
Evolutionary Reinforcement Learning: A Survey
Reinforcement learning (RL) is a machine learning approach that trains agents
to maximize cumulative rewards through interactions with environments. The
integration of RL with deep learning has recently resulted in impressive
achievements in a wide range of challenging tasks, including board games,
arcade games, and robot control. Despite these successes, there remain several
crucial challenges, including brittle convergence properties caused by
sensitive hyperparameters, difficulties in temporal credit assignment with long
time horizons and sparse rewards, a lack of diverse exploration, especially in
continuous search space scenarios, difficulties in credit assignment in
multi-agent reinforcement learning, and conflicting objectives for rewards.
Evolutionary computation (EC), which maintains a population of learning agents,
has demonstrated promising performance in addressing these limitations. This
article presents a comprehensive survey of state-of-the-art methods for
integrating EC into RL, referred to as evolutionary reinforcement learning
(EvoRL). We categorize EvoRL methods according to key research fields in RL,
including hyperparameter optimization, policy search, exploration, reward
shaping, meta-RL, and multi-objective RL. We then discuss future research
directions in terms of efficient methods, benchmarks, and scalable platforms.
This survey serves as a resource for researchers and practitioners interested
in the field of EvoRL, highlighting the important challenges and opportunities
for future research. With the help of this survey, researchers and
practitioners can develop more efficient methods and tailored benchmarks for
EvoRL, further advancing this promising cross-disciplinary research field
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