Reinforcement Learning (RL) is a popular machine learning paradigm where intelligent
agents interact with the environment to fulfill a long-term goal. Driven by the resurgence of …

Learning rational behaviors in open-world games like Minecraft remains to be challenging
for Reinforcement Learning (RL) research due to the compound challenge of partial …

The black-box nature of deep reinforcement learning (RL) hinders them from real-world
applications. Therefore, interpreting and explaining RL agents have been active research …

As we move into the next stages of the technological revolution, artificial intelligence (AI) that
is explainable and sustainable is becoming a key goal for researchers across multiple …

In multiagent learning, one of the main ways to improve learning performance is to ask for
advice from another agent. Contemporary advising methods share a common limitation that …

Reward shaping using GCNs is a popular research area in reinforcement learning.
However, it is difficult to shape potential functions for complicated tasks. In this paper, we …

Reinforcement learning faces the challenge of sparse rewards. Existing research utilizes
reward shaping based on graph convolutional neural networks (GCNs) to address this …

Many applications of reinforcement learning can be formalized as goal-conditioned
environments, where, in each episode, there is a" goal" that affects the rewards obtained …

Deep reinforcement learning (DRL) has achieved great success in recent years by
combining the feature extraction power of deep learning and the decision power of …

Visual navigation based on deep reinforcement learning requires a large amount of
interaction with the environment, and due to the reward sparsity, it requires a large amount …