Analysis of Evolutionary Dynamics for Bidding Strategy Driven by Multi-Agent Reinforcement Learning

Multi-Agent Reinforcement Learning with Bidding for Segmenting Action Sequences

Analyzing Multi-agent Reinforcement Learning Using Evolutionary Dynamics

In this paper, we show how the dynamics of Q-learning can be visualized and analyzed from a perspective of Evolutionary Dynamics (ED). More specifically, we show how ED can be used as a model for Qlearning in stochastic games. Analysis of the evolutionary stable strategies and attractors of the derived ED from the Reinforcement Learning (RL) application then predict the desired parameters for R...

The Dynamics of Multi-Agent Reinforcement Learning

Infinite-horizon multi-agent control processes with nondeterminism and partial state knowledge have particularly interesting properties with respect to adaptive control, such as the non-existence of Nash Equilibria (NE) or non-strict NE which are nonetheless points of convergence. The identification of reinforcement learning (RL) algorithms that are robust, accurate and efficient when applied t...

Deep Reinforcement Learning for Event-Driven Multi-Agent Decision Processes

The incorporation of macro-actions (temporally extended actions) into multi-agent decision problems has the potential to address the curse of dimensionality associated with such decision problems. Since macro-actions last for stochastic durations, multiple agents executing decentralized policies in cooperative environments must act asynchronously. We present an algorithm that modifies Generaliz...

Multi-Agent Reinforcement Learning

This thesis presents a novel approach to provide adaptive mechanisms to detect and categorise Flooding-Base DoS (FBDoS) and Flooding-Base DDoS (FBDDoS) attacks. These attacks are generally based on a flood of packets with the intention of overfilling key resources of the target, and today the attacks have the capability to disrupt networks of almost any size. To address this problem we propose ...