We study the constrained reinforcement learning problem, in which an agent aims tomaximize the expected cumulativereward subject toaconstraint on the expected total value of a utility function. In contrast to existing model-based approaches or model-free methods accompanied with a'simulator', we aim to develop thefirst model-free, simulator-freealgorithm that achieves a sublinear regret and a sublinear constraint violation even inlarge-scale systems.

We study the constrained reinforcement learning problem, in which an agent aims tomaximize the expected cumulativereward subject toaconstraint on the expected total value of a utility function. In contrast to existing model-based approaches or model-free methods accompanied with a'simulator', we aim to develop thefirst model-free, simulator-freealgorithm that achieves a sublinear regret and a sublinear constraint violation even inlarge-scale systems.

Note that these games are two-player games, we select the runner in You-Shall-Not-Pass and kicker in Kick-And-Defend as our target agent. Section 4 mentioned that we download a well-trained policy for each game.

Deep reinforcement learning has shown great success in automatic policy learning for various sequential decision-making problems, such as training AI agents to defeat professional players in sophisticated games [74, 65, 24, 37] and controlling robots to accomplish complicated tasks [33, 38].

However, FACMAClearnsacentralised butfactored critic,which combines per-agent utilities into the joint action-value function via a non-linear monotonic function, as inQMIX, apopular multi-agentQ-learning algorithm. However,unlikeQMIX, there are no inherent constraints on factoring the critic. We thus also employ a nonmonotonic factorisation and empirically demonstrate that its increased representational capacity allows it to solve some tasks that cannot be solved with monolithic, ormonotonically factored critics.

We consider the problem of contextual bandits and imitation learning, where the learner lacks direct knowledge of the executed action's reward.