Motion planning for autonomous racing is a challenging task due to the safety requirement
while driving aggressively. Most previous solutions utilize the prior information or depend on …

Growth in the penetration of renewable energy sources makes supply more uncertain and
leads to an increase in the system imbalance. This trend, together with the single imbalance …

Many Deep Reinforcement Learning (D-RL) algorithms rely on simple forms of exploration
such as the additive action noise often used in continuous control domains. Typically, the …

Despite recent advances in improving the sample-efficiency of reinforcement learning (RL)
algorithms, designing an RL algorithm that can be practically deployed in real-world …

World models learn behaviors in a latent imagination space to enhance the sample-
efficiency of deep reinforcement learning (RL) algorithms. While learning world models for …

In this paper, we propose a novel Reinforcement Learning (RL) framework for problems with
continuous action spaces: Action Quantization from Demonstrations (AQuaDem). The …

While there has been substantial success for solving continuous control with actor-critic
methods, simpler critic-only methods such as Q-learning find limited application in the …

In reinforcement learning (RL), simplicity is typically quantified on an action-by-action basis--
but this timescale ignores temporal regularities, like repetitions, often present in sequential …

We propose a new policy parameterization for representing 3D rotations during
reinforcement learning. Today in the continuous control reinforcement learning literature …

Autonomous racing is a challenging task that requires vehicle handling at the dynamic limits
of friction. While single-agent scenarios like Time Trials are solved competitively with …