Multi-agent reinforcement learning (MARL) has emerged as a useful approach to solving
decentralised decision-making problems at scale. Research in the field has been growing …

Stock value prediction and trading, a captivating and complex research domain, continues to
draw heightened attention. Ensuring profitable returns in stock market investments demands …

Offline training in simulated partially observable environments allows reinforcement learning
methods to exploit privileged state information through a mechanism known as asymmetry …

In partially observable reinforcement learning, offline training gives access to latent
information which is not available during online training and/or execution, such as the …

Multi-agent deep reinforcement learning (MDRL) has attracted attention for solving complex
tasks. Two main challenges of MDRL are non-stationarity and partial observability from the …

Abstract Purpose of Review To effectively synthesise and analyse multi-robot behaviour, we
require formal task-level models which accurately capture multi-robot execution. In this …

In centralized multi-agent systems, often modeled as multi-agent partially observable
Markov decision processes (MPOMDPs), the action and observation spaces grow …

Even though partial observability is prevalent in robotics, most reinforcement learning
studies avoid it due to the difficulty of learning a policy that can efficiently memorize past …