Optimistic Policy Learning under Pessimistic Adversaries with Regret and Violation Guarantees

Create an RL simulation environment that demonstrates robust agent learning against strategic adversaries. Focus on providing clean baseline implementations of optimistic policy learning with regret guarantees.

Suggested repo: adversarialRL

"Train agents that don't crack under pessimistic adversary pressure."

Estimated effort: 40h