In a dynamic environment, the moving obstacle makes the path planning of the manipulator
very difficult. Therefore, this paper proposes a path planning with dynamic obstacle …

In this paper, we propose an end-to-end modular reinforcement learning architecture for a
navigation task in complex dynamic environments with rapidly moving obstacles. In this …

In this paper, we propose a goal-oriented obstacle avoidance navigation system based on
deep reinforcement learning that uses depth information in scenes, as well as goal position …

Abstract Deep Q Network (DQN) plays a crucial role in path planning for autonomous mobile
robots. The traditional DQN algorithm has problems such as slow convergence speed and …

Despite the benefits mentioned in previous works of automatically acquiring skills for using
them in hierarchical reinforcement learning algorithms such as solving the curse of …

Hierarchical reinforcement learning works on temporally extended actions or skills to
facilitate learning. How to automatically form such abstraction is challenging, and many …

With the dominant trope of the computer as adversary rather than enabler, reinforcement
learning for games has mainly focused on the ability of agents to compete and win. Although …

Despite the benefits mentioned in previous works of automatically acquiring skills for using
them in hierarchical reinforcement learning algorithms such as solving the curse of …

Today, we strive to create better and better human beings every day, in every field. Games
have existed in human history ever since a long time and are responsible for developing …

Exploration has shown to be difficult in games where the reward space is sparse. The agent
has trouble reaching any reward and therefore cannot learn a good policy. One recent …