Reinforcement learning (RL) algorithms find applications in inventory control, recommender
systems, vehicular traffic management, cloud computing, and robotics. The real-world …

Multi-agent learning (MAL) studies how agents learn to behave optimally and adaptively
from their experience when interacting with other agents in dynamic environments. The …

The key challenge in multiagent learning is learning a best response to the behaviour of
other agents, which may be non-stationary: if the other agents adapt their strategy as well …

Reinforcement learning (RL) methods learn optimal decisions in the presence of a stationary
environment. However, the stationary assumption on the environment is very restrictive. In …

As humans, our goals and our environment are persistently changing throughout our lifetime
based on our experiences, actions, and internal and external drives. In contrast, typical …

As humans, our goals and our environment are persistently changing throughout our lifetime
based on our experiences, actions, and internal and external drives. In contrast, typical …

In cognitive radio (CR) networks, fast and accurate spectrum sensing plays a fundamental
role in achieving high spectral efficiency. In this paper, a reinforcement learning (RL) …

Deep reinforcement learning (DRL) is a powerful tool for learning from interactions within a
stationary environment where state transition and reward distributions remain constant …

Agents must be able to adapt quickly as an environment changes. We find that existing
model-based reinforcement learning agents are unable to do this well, in part because of …

Non-stationary environments are challenging for reinforcement learning algorithms. If the
state transition and/or reward functions change based on latent factors, the agent is …