… Reinforcement Learning (RL) for achieving superhuman performance in practice. However,
the majority of exisiting theory in reinforcement learning … in competitive reinforcement learn…

… We obtain global, non-asymptotic convergence guarantees for independent learning
algorithms in competitive reinforcement learning settings with two agents (ie, zero-sum stochastic …

… This paper introduces Honor of Kings Arena, a reinforcement learning (RL) environment … ,
ours presents new generalization challenges for competitive reinforcement learning. It is a multi…

… art reinforcement learning algorithm to learn to perform multiple tasks. We demonstrate that
the limitation of learning to performing two tasks can be mitigated with a competitive training …

… a competitive and cooperative heterogeneous deep reinforcement learning framework …
Moreover, to guarantee sample e ciency, we propose a competitive mechanism to dynamically …

… of a two-unit competitive reinforcement network 3 Demonstration This section contains the
results of two experiments we performed using the competitive reinforcement algorithm. The …

… two-agent competitive reinforcement learning. Competitive reinforcement learning has been
… first backdoor attack targeted at competitive reinforcement learning systems. The trigger is …

… the competitive leaming rule found in Learning Vector Quantization (LVQ) serves as a promising
function approximator to enable reinforcement learning … , a novel reinforcement learning …

… We propose a new algorithm, called Anytime-Competitive Reinforcement Learning (ACRL), …
optimal reward achievable under the anytime competitive constraints. Experiments on the …

… While real-world applications of reinforcement learning (RL) are becoming popular, the …
door Detection in a multi-agent competitive reinforcement learning system, with the objective of …