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An overview of multi-agent reinforcement learning from game theoretical perspective
Y Yang, J Wang - ar** stone to this goal, the domain of StarCraft …
Adversarial policies: Attacking deep reinforcement learning
Deep reinforcement learning (RL) policies are known to be vulnerable to adversarial
perturbations to their observations, similar to adversarial examples for classifiers. However …
perturbations to their observations, similar to adversarial examples for classifiers. However …
From motor control to team play in simulated humanoid football
Learning to combine control at the level of joint torques with longer-term goal-directed
behavior is a long-standing challenge for physically embodied artificial agents. Intelligent …
behavior is a long-standing challenge for physically embodied artificial agents. Intelligent …
Scalable evaluation of multi-agent reinforcement learning with melting pot
Existing evaluation suites for multi-agent reinforcement learning (MARL) do not assess
generalization to novel situations as their primary objective (unlike supervised learning …
generalization to novel situations as their primary objective (unlike supervised learning …
The ai economist: Improving equality and productivity with ai-driven tax policies
Tackling real-world socio-economic challenges requires designing and testing economic
policies. However, this is hard in practice, due to a lack of appropriate (micro-level) …
policies. However, this is hard in practice, due to a lack of appropriate (micro-level) …
Multi-objective multi-agent decision making: a utility-based analysis and survey
The majority of multi-agent system implementations aim to optimise agents' policies with
respect to a single objective, despite the fact that many real-world problem domains are …
respect to a single objective, despite the fact that many real-world problem domains are …
Adversarial policies beat superhuman go AIs
We attack the state-of-the-art Go-playing AI system KataGo by training adversarial policies
against it, achieving a $> $97% win rate against KataGo running at superhuman settings …
against it, achieving a $> $97% win rate against KataGo running at superhuman settings …