Offline safe reinforcement learning (RL) aims to train a constraint satisfaction policy from a
fixed dataset. Current state-of-the-art approaches are based on supervised learning with a …

Reinforcement learning (RL) is pivotal in empowering Unmanned Aerial Vehicles (UAVs) to
navigate and make decisions efficiently and intelligently within complex and dynamic …

In the domain of reinforcement learning (RL), deriving efficacious state representations and
maintaining algorithmic stability are crucial for optimal agent performance. However, the …

When the requirements for water pressure and quantity of drinking water for residents and
industrial buildings exceed the capacity of the urban water distribution system, a secondary …

Agents often need a long time to explore state-action space in order to learn how to act
expectedly in Partially Observable Markov Decision Processes (POMDPs). With the reward …

To ensure the steady navigation for robot stable controls are one of the basic requirements.
Control values selection is highly environment dependent. To ensure reusability of control …

Many imitation learning (IL) algorithms employ inverse reinforcement learning (IRL) to infer
the underlying reward function that an expert is implicitly optimizing for, based on their …