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 …

Reinforcement Learning (RL) has proven to be highly effective in various real-world
applications. However, in certain scenarios, Evolutionary Algorithms (EAs) have been …

Deep neuroevolution and deep Reinforcement Learning have received a lot of attention
over the past few years. Some works have compared them, highlighting their pros and cons …

Twin delayed deep deterministic (TD3) policy gradient is an effective algorithm for
continuous action spaces. However, it cannot efficiently explore the spatial space and …

While off-policy reinforcement learning (RL) algorithms are sample efficient due to gradient-
based updates and data reuse in the replay buffer, they struggle with convergence to local …

Power grids, across the world, play an important societal and economical role by providing
uninterrupted, reliable and transient-free power to several industries, businesses and …

Abstract Cross-Entropy Method (CEM) is commonly used for planning in model-based
reinforcement learning (MBRL) where a centralized approach is typically utilized to update …

We investigate artificial intelligence and machine learning methods for optimizing the
adversarial behavior of agents in cybersecurity simulations. Our cybersecurity simulations …

Abstract Evolutionary Algorithms (EAs) and Deep Reinforcement Learning (DRL) have
recently been integrated to take advantage of both methods for better exploration and …

Generative Flow Networks (GFlowNets) are a family of probabilistic generative models that
learn to sample compositional objects proportional to their rewards. One big challenge of …