Deep reinforcement learning (RL) uses model-free techniques to optimize task-specific
control policies. Despite having emerged as a promising approach for complex problems …

In order to enhance performance of robot systems in the manufacturing industry, it is
essential to develop motion and task planning algorithms. Especially, it is important for the …

Reinforcement learning (RL) enables agents to take decision based on a reward function.
However, in the process of learning, the choice of values for learning algorithm parameters …

Due to the sparse rewards and high degree of environmental variation, reinforcement
learning approaches, such as deep deterministic policy gradient (DDPG), are plagued by …

In this article, an expert system-based multiagent deep deterministic policy gradient (ESB-
MADDPG) is proposed to realize the decision making for swarm robots. Multiagent deep …

Deep reinforcement learning (DRL) has recently received a lot of attention due to its better
performance compared to traditional algorithms in mapless navigation tasks. However …

Researchers have made a lot of progress in combining the advances in Deep Learning and
the generalization and applicability of Reinforcement learning to the sequential decision …

Deep reinforcement learning, by taking advantage of neural networks, has made great
strides in the continuous control of robots. However, in scenarios where multiple robots are …

Since path planning for multi-arm manipulators is a complicated high-dimensional problem,
effective and fast path generation is not easy for the arbitrarily given start and goal locations …

Sudden changes in the dynamics of robotic tasks, such as contact with an object or the
latching of a door, are often viewed as inconvenient discontinuities that make manipulation …