Robust Reinforcement Learning with Wasserstein Constraint

Understand

Robust Reinforcement Learning aims to find the optimal policy with some extent of robustness to environmental dynamics.

• Existing learning algorithms usually enable the robustness through disturbing the current state or simulating environmental parameters in a heuristic way, which lack quantified robustness to the system dynamics (i.e.
• transition probability).
• To overcome this issue, we leverage Wasserstein distance to measure the disturbance to the reference transition kernel.