This comprehensive survey explored the evolving landscape of generative Artificial
Intelligence (AI), with a specific focus on the transformative impacts of Mixture of Experts …

Learned locomotion policies can rapidly adapt to diverse environments similar to those
experienced during training but lack a mechanism for fast tuning when they fail in an out-of …

We consider solving sequential decision-making problems in the scenario where the agent
has access to two supervision sources: $\textit {reward signal} $ and a $\textit {teacher} …

Abstract We present AIRS: Automatic Intrinsic Reward Shaping that intelligently and
adaptively provides high-quality intrinsic rewards to enhance exploration in reinforcement …

Training a deep neural network to maximize a target objective has become the standard
recipe for successful machine learning over the last decade. These networks can be …

Deep reinforcement learning-based (DRL-based) path planning in the unknown
environment is studied under continuous action space. We extend the TD3 (twin-delayed …

Many roboticists dream of presenting a robot with a task in the evening and returning the
next morning to find the robot capable of solving the task. What is preventing us from …

In many real-world problems, an agent must operate in an uncertain and partially
observable environment. Due to partial information, a policy directly trained to operate from …

A novel method, the Pareto Envelope Augmented with Reinforcement Learning (PEARL),
has been developed to address the challenges posed by multi-objective problems …

The ability to efficiently explore high-dimensional state spaces is essential for the practical
success of deep Reinforcement Learning (RL). This paper introduces a new exploration …