Existing motion planning methods often have two drawbacks:(1) goal configurations need to
be specified by a user, and (2) only a single solution is generated under a given condition. In …

Conventional hierarchical reinforcement learning (HRL) relies on discrete options to
represent explicitly distinguishable knowledge, which may lead to severe performance …

Legged locomotion is a challenging task for learning algorithms, especially when the task
requires a diverse set of primitive behaviors. To solve these problems, we introduce a …

We investigate the challenge of parametrizing policies for reinforcement learning (RL) in
high-dimensional continuous action spaces. Our objective is to develop a multimodal policy …

Reinforcement learning algorithms are typically limited to learning a single solution for a
specified task, even though diverse solutions often exist. Recent studies showed that …

We propose a novel reinforcement learning algorithm, AlphaNPI, that incorpo-rates the
strengths of Neural Programmer-Interpreters (NPI) and AlphaZero. NPI contributes structural …

The objective function used in trajectory optimization is often non-convex and can have an
infinite set of local optima. In such cases, there are diverse solutions to perform a given task …

Visual navigation in unknown environments poses significant challenges due to the
presence of many obstacles and low-texture scenes. These factors may cause frequent …

Transfer reinforcement learning (RL) aims at improving the learning efficiency of an agent by
exploiting knowledge from other source agents trained on relevant tasks. However, it …

In this study, we investigate the use of global information to speed up the learning process
and increase the cumulative rewards of reinforcement learning (RL) in competition tasks …