Deep Reinforcement Learning: A Convex Optimization Approach
This provides a fast, convergent method for stable nonlinear systems in RL, but it is incremental as it builds on existing convex optimization techniques.
The paper tackles reinforcement learning for nonlinear systems with continuous spaces by using convex optimization to train a two-layer neural network approximating the Q-function, showing that the algorithm converges with parameters arbitrarily close to optimal, bounded by O(ρ) as regularization decreases.
In this paper, we consider reinforcement learning of nonlinear systems with continuous state and action spaces. We present an episodic learning algorithm, where we for each episode use convex optimization to find a two-layer neural network approximation of the optimal $Q$-function. The convex optimization approach guarantees that the weights calculated at each episode are optimal, with respect to the given sampled states and actions of the current episode. For stable nonlinear systems, we show that the algorithm converges and that the converging parameters of the trained neural network can be made arbitrarily close to the optimal neural network parameters. In particular, if the regularization parameter in the training phase is given by $ρ$, then the parameters of the trained neural network converge to $w$, where the distance between $w$ and the optimal parameters $w^\star$ is bounded by $\mathcal{O}(ρ)$. That is, when the number of episodes goes to infinity, there exists a constant $C$ such that \[ \|w-w^\star\| \le Cρ. \] In particular, our algorithm converges arbitrarily close to the optimal neural network parameters as the regularization parameter goes to zero. As a consequence, our algorithm converges fast due to the polynomial-time convergence of convex optimization algorithms.