In many application areas, Deep Reinforcement Learning (DRL) has led to breakthroughs.
In Curriculum Learning, the Machine Learning algorithm is not randomly presented with …

Deep reinforcement learning (DRL) has shown promising performance in various
application areas (eg, games and autonomous vehicles). Experience replay buffer strategy …

Deep reinforcement learning (DRL) methods can solve complex continuous control tasks in
simulated environments by taking actions based solely on state observations at each …

Deep Reinforcement Learning (DRL) is considered a potential framework to improve many
real-world autonomous systems; it has attracted the attention of multiple and diverse fields …

Deep Reinforcement Learning (DRL) has been applied successfully to many robotic
applications. However, the large number of trials needed for training is a key issue. Most of …

The increasing trend of studying the innate softness of robotic structures and amalgamating
it with the benefits of the extensive developments in the field of embodied intelligence has …

Reinforcement learning (rl) is a popular paradigm for sequential decision making problems.
The past decade's advances in rl have led to breakthroughs in many challenging domains …

Deep reinforcement learning (DRL) agents are often sensitive to visual changes that were
unseen in their training environments. To address this problem, we leverage the sequential …

Reinforcement learning has proven successful in games, but suffers from long training times
when compared to other forms of machine learning. Curriculum learning, an optimisation …

Applying Deep Reinforcement Learning (DRL) to complex tasks in the field of robotics has
proven to be very successful in the recent years. However, most of the publications focus …