Multi-Agent Reinforcement Learning with Optimal Equivalent Action of Neighborhood

Multi-Agent Reinforcement Learning with Bidding for Segmenting Action Sequences

Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal Demonstrations

This paper considers the problem of inverse reinforcement learning in zero-sum stochastic games when expert demonstrations are known to be not optimal. Compared to previous works that decouple agents in the game by assuming optimality in expert strategies, we introduce a new objective function that directly pits experts against Nash Equilibrium strategies, and we design an algorithm to solve fo...

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 ...

Multi-Agent Reinforcement Learning with Vicarious Rewards

Reinforcement learning is the problem faced by an agent that must learn behaviour through trial-and-error interactions with a dynamic environment. In a multi-agent setting, the problem is often further complicated by the need to take into account the behaviour of other agents in order to learn to perform effectively. Issues of coordination and cooperation must be addressed; in general, it is no...

Multi-Agent Deep Reinforcement Learning

This work introduces a novel approach for solving reinforcement learning problems in multi-agent settings. We propose a state reformulation of multi-agent problems in R that allows the system state to be represented in an image-like fashion. We then apply deep reinforcement learning techniques with a convolution neural network as the Q-value function approximator to learn distributed multi-agen...