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Exploring applications of deep reinforcement learning for real-world autonomous driving systems
(2019-01)
Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years, with notable achievements
such as Deepmind’s AlphaGo. It has been successfully deployed in commercial vehicles like Mobileye’s
path ...
Multi-Agent Credit Assignment in Stochastic Resource Management Games
(2017-08)
Multi-Agent Systems (MAS) are a form of distributed intelligence, where multiple autonomous
agents act in a common environment. Numerous complex, real world systems have been
successfully optimised using Multi-Agent ...
Vulnerable road user detection: state-of-the-art and open challenges
(2019)
Correctly identifying vulnerable road users (VRUs), e.g. cyclists and pedestrians, remains one of the most challenging environment perception tasks for autonomous vehicles (AVs). This work surveys the current state-of-the-art ...
Analysing the Effects of Reward Shaping in Multi-Objective Stochastic Games
(2017-05)
The majority ofMulti-Agent Reinforcement Learning (MARL)
implementations aim to optimise systems with respect to a
single objective, despite the fact that many real world problems
are inherently multi-objective in nature. ...
Potential-Based Reward Shaping Preserves Pareto Optimal Policies
(2017-05)
Reward shaping is a well-established family of techniques
that have been successfully used to improve the performance
and learning speed of Reinforcement Learning agents in singleobjective
problems. Here we extend the ...
A Theoretical and Empirical Analysis of Reward Transformations in Multi-Objective Stochastic Games
(2017)
Reward shaping has been proposed as a means to address the
credit assignment problem in Multi-Agent Systems (MAS).
Two popular shaping methods are Potential-Based Reward
Shaping and di erence rewards, and both have been ...
Policy Invariance under Reward Transformations for Multi-Objective Reinforcement Learning
(2017)
Reinforcement Learning (RL) is a powerful and well-studied Machine Learning
paradigm, where an agent learns to improve its performance in an environment
by maximising a reward signal. In multi-objective Reinforcement
Learning ...
Curriculum Learning for Tightly Coupled Multiagent Systems
(2019)
In this paper,we leverage curriculum learning(CL) to improve the performance of multiagent systems(MAS) that are trained
with the cooperative coevolution of artificial neural networks. We design curricula to progressively ...
Equilibria in Multi-Objective Games: a Utility-Based Perspective
(Adaptive and Learning Agents Workshop, 2019)
In multi-objective multi-agent systems (MOMAS), agents explicitly
consider the possible tradeoffs between conflicting objective functions.
We argue that compromises between competing objectives
in MOMAS should be analysed ...