constrained-mdps

Constrained Policy Optimization

In reinforcement learning (RL), agents learn to act by trial and error, gradually improving their performance at the For many applications of reinforcement learn- task as learning progresses. Recent work in deep RL as- ing it can be more convenient to specify both sumes that agen...

Safe Model-Based Multi-Agent Mean-Field Reinforcement Learning

Many applications, e.g., in shared mobility, require coordinating a large number of agents. Mean-field reinforcement learning ad- dresses the resulting scalability challenge by optimizing the policy of a representative agent interacting with the infinite population of identical a...