Actor-critic deep reinforcement learning (DRL) algorithms have recently achieved prominent
success in tackling various challenging reinforcement learning (RL) problems, particularly …

Value-based methods tend to outperform policy optimization methods when trained and
tested in single environments; however, they significantly underperform when trained on …

While current benchmark reinforcement learning (RL) tasks have been useful to drive
progress in the field, they are in many ways poor substitutes for learning with real-world …

Reinforcement Learning (RL) is a branch of Artificial Intelligence (AI) which focuses on
training agents to make decisions by interacting with their environment to maximize …

Recent methods using Reinforcement Learning (RL) have proven to be successful for
training intelligent agents in unknown environments. However, RL has not been applied …

Deep reinforcement learning (DRL) faces significant challenges in addressing hard-
exploration tasks with sparse or deceptive rewards and large state spaces. These …

Model-based reinforcement learning (MBRL) has been proposed as a promising alternative
solution to tackle the high sampling cost challenge in the canonical reinforcement learning …

Reinforcement learning (RL) interacts with the environment to solve sequential decision-
making problems via a trial-and-error approach. Errors are always undesirable in real-world …

Model-free Reinforcement Learning (RL) generally suffers from poor sample complexity,
mostly due to the need to exhaustively explore the state-action space to find well-performing …