The reinforcement learning (RL) research area is very active, with an important number of
new contributions, especially considering the emergent field of deep RL (DRL). However, a …

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 …

We introduce a new unsupervised pre-training method for reinforcement learning called
APT, which stands for Active Pre-Training. APT learns behaviors and representations by …

Recent exploration methods have proven to be a recipe for improving sample-efficiency in
deep reinforcement learning (RL). However, efficient exploration in high-dimensional …

Deep reinforcement learning (DRL) and deep multiagent reinforcement learning (MARL)
have achieved significant success across a wide range of domains, including game artificial …

Deep reinforcement learning (DRL) and evolution strategies (ESs) have surpassed human-
level control in many sequential decision-making problems, yet many open challenges still …

A promising technique for exploration is to maximize the entropy of visited state distribution,
ie, state entropy, by encouraging uniform coverage of visited state space. While it has been …

In the absence of assigned tasks, a learning agent typically seeks to explore its environment
efficiently. However, the pursuit of exploration will bring more safety risks. An under-explored …

Exploration methods based on pseudo-count of transitions or curiosity of dynamics have
achieved promising results in solving reinforcement learning with sparse rewards. However …

The human intrinsic desire to pursue knowledge, also known as curiosity, is considered
essential in the process of skill acquisition. With the aid of artificial curiosity, we could equip …