Evolutionary reinforcement learning promises further advances in machine learning

Newswise — Evolutionary reinforcement learning is a thrilling frontier in ML, fusing the merits of reinforcement learning and evolutionary computation. Within this domain, a smart agent acquires optimal strategies by actively exploring varied methods and gaining rewards for successful outcomes. This inventive approach merges the trial-and-error learning of reinforcement learning with the natural selection mimicry of evolutionary algorithms. The result is a potent AI development methodology that holds the potential for major advancements across diverse fields.

An epoch-making survey article on evolutionary reinforcement learning was released on Apr. 21 in Intelligent Computing, a Science Partner Journal. It illuminates the most recent progress in the fusion of evolutionary computation and reinforcement learning, while offering an extensive overview of cutting-edge techniques.

Reinforcement learning, a subset of machine learning, concentrates on constructing algorithms that acquire decision-making abilities through feedback received from the surrounding environment. Noteworthy instances of triumphant reinforcement learning encompass AlphaGo and, more recently, soccer-playing robots developed by Google DeepMind. Nevertheless, despite these achievements, reinforcement learning confronts various hurdles such as balancing exploration and exploitation, designing appropriate rewards, achieving generalization, and assigning credit accurately.

Evolutionary computation, an approach that mimics natural evolution to tackle problems, presents a promising solution to the challenges faced by reinforcement learning. Through the integration of these two methodologies, the field of evolutionary reinforcement learning emerged, enabling researchers to leverage the strengths of both approaches in unison.

Evolutionary reinforcement learning encompasses six key research areas:

• Hyperparameter optimization: Evolutionary computing techniques can be employed for optimizing hyperparameters. In other words, they can automatically determine the optimal configurations for reinforcement learning systems. Manual exploration of these settings can be difficult due to the numerous factors at play, such as the algorithm's learning rate and its preference for long-term rewards. Additionally, the performance of reinforcement learning heavily depends on the neural network architecture utilized, including aspects like layer count and size.
• Policy search involves exploring various strategies for a task to identify the optimal approach, with the assistance of neural networks. These networks, acting as powerful computational tools, approximate task execution and leverage advancements in deep learning. Given the multitude of possible task execution paths, the search process resembles navigating through an extensive maze. Stochastic gradient descent is a popular technique for training neural networks and navigating this maze. However, evolutionary computing provides alternative "neuroevolution" methods that utilize evolution strategies, genetic algorithms, and genetic programming. These methods excel at determining the optimal weights and other attributes of neural networks for reinforcement learning.
• Exploration is a vital aspect of reinforcement learning, where agents enhance their performance by actively engaging with the environment. Insufficient exploration may result in suboptimal decisions, while excessive exploration can incur unnecessary costs. Consequently, there exists a trade-off between an agent's exploration to discover favorable behaviors and its exploitation of the already discovered ones. Agents achieve exploration by incorporating randomness into their actions. However, efficient exploration encounters several challenges, including a vast array of potential actions, rare and delayed rewards, unpredictable environments, and intricate multi-agent scenarios. Evolutionary computation methods address these challenges by fostering competition, cooperation, and parallelization. They promote exploration by maintaining diversity within populations and employing guided evolution techniques.
• Reward shaping plays a crucial role in reinforcement learning as rewards are essential but can be sparse and difficult for agents to learn from. To address this issue, reward shaping involves incorporating additional finely-tuned rewards to facilitate improved learning for agents. However, these additional rewards may inadvertently impact agents' behavior in undesirable ways. Moreover, determining the appropriate nature of these extra rewards, achieving the right balance, and correctly attributing credit across multiple agents typically necessitate task-specific knowledge. To tackle the challenges associated with reward design, researchers have turned to evolutionary computation methods to adjust the extra rewards and their settings in both single-agent and multi-agent reinforcement learning scenarios. This approach aids in refining and optimizing the reward structure to enhance learning outcomes.
• Meta-reinforcement learning aims to create a versatile learning algorithm that can adapt to different tasks by leveraging knowledge acquired from previous tasks. This approach tackles the challenge of requiring an extensive number of samples to learn each task from scratch in traditional reinforcement learning. However, the scope and complexity of tasks that can be effectively solved using meta-reinforcement learning are still limited, and the associated computational costs can be high. Therefore, harnessing the model-agnostic and highly parallel characteristics of evolutionary computation presents a promising avenue to unlock the full potential of meta-reinforcement learning. By incorporating evolutionary computation, meta-reinforcement learning can enhance its learning capabilities, improve generalization to new tasks, and become more computationally efficient in real-world scenarios.
• In certain real-world scenarios, multiple objectives exist that may conflict with one another. To address this, multi-objective reinforcement learning techniques are employed. These methods leverage multi-objective evolutionary algorithms to strike a balance between conflicting goals and propose compromise solutions when no single solution outperforms the others. Multi-objective reinforcement learning approaches can be categorized into two types: those that aggregate multiple goals into a single best solution and those that aim to identify a range of satisfactory solutions. Conversely, in some cases, breaking down a single-goal problem into multiple objectives can facilitate easier problem-solving and yield useful outcomes.

Evolutionary reinforcement learning has demonstrated its effectiveness in addressing intricate reinforcement learning tasks, including scenarios with scarce or misleading rewards. Nevertheless, it is important to acknowledge that this approach often demands substantial computational resources, resulting in high computational costs. Given this, there is a rising demand for more efficient methods within the field. Researchers are actively exploring avenues for improvement, including advancements in encoding techniques, more efficient sampling approaches, enhanced search operators, refined algorithmic frameworks, and improved evaluation methodologies. These efforts aim to optimize the efficiency of evolutionary reinforcement learning and make it more accessible for a wider range of applications.

Indeed, evolutionary reinforcement learning has demonstrated promise in tackling challenging reinforcement learning problems. However, there is still room for further advancements and improvements in the field. Enhancing the computational efficiency of evolutionary methods can lead to more scalable and practical solutions. Researchers can also explore new benchmarks, platforms, and applications to test and expand the capabilities of evolutionary reinforcement learning algorithms. By pushing the boundaries and continuously innovating, researchers can make evolutionary methods even more effective and valuable for addressing complex reinforcement learning tasks.