Continuous-Time Fitted Value Iteration for Robust Policies
This work addresses robust policy optimization for continuous control in domains like robotics, offering incremental improvements by simplifying differential equations for specific problem structures.
The paper tackles the problem of solving Hamilton-Jacobi-Bellman and Hamilton-Jacobi-Isaacs equations for continuous control by proposing continuous fitted value iteration (cFVI) and robust fitted value iteration (rFVI), which derive optimal and robust policies in closed form without discretization, achieving successful real-world task completion in experiments on the Furuta pendulum and cartpole with improved robustness compared to non-robust and deep reinforcement learning methods.
Solving the Hamilton-Jacobi-Bellman equation is important in many domains including control, robotics and economics. Especially for continuous control, solving this differential equation and its extension the Hamilton-Jacobi-Isaacs equation, is important as it yields the optimal policy that achieves the maximum reward on a give task. In the case of the Hamilton-Jacobi-Isaacs equation, which includes an adversary controlling the environment and minimizing the reward, the obtained policy is also robust to perturbations of the dynamics. In this paper we propose continuous fitted value iteration (cFVI) and robust fitted value iteration (rFVI). These algorithms leverage the non-linear control-affine dynamics and separable state and action reward of many continuous control problems to derive the optimal policy and optimal adversary in closed form. This analytic expression simplifies the differential equations and enables us to solve for the optimal value function using value iteration for continuous actions and states as well as the adversarial case. Notably, the resulting algorithms do not require discretization of states or actions. We apply the resulting algorithms to the Furuta pendulum and cartpole. We show that both algorithms obtain the optimal policy. The robustness Sim2Real experiments on the physical systems show that the policies successfully achieve the task in the real-world. When changing the masses of the pendulum, we observe that robust value iteration is more robust compared to deep reinforcement learning algorithm and the non-robust version of the algorithm. Videos of the experiments are shown at https://sites.google.com/view/rfvi