Adaptive Dynamic Programming Control
265 papers on this topic
Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation
The shortcomings of maximum likelihood estimation in the context of model-based reinforcement learning have been highlighted by an increasing number of papers. When the model class is misspecified...
Stein Variational Model Predictive Control
Decision making under uncertainty is critical to real-world, autonomous systems. Model Predictive Control (MPC) methods have demonstrated favorable performance in practice, but remain limited when...
Mirror Descent Policy Optimization
Mirror descent (MD), a well-known first-order method in constrained convex optimization, has recently been shown as an important tool to analyze trust-region algorithms in reinforcement learning...
On the Estimation Bias in Double Q-Learning
Double Q-learning is a classical method for reducing overestimation bias, which is caused by taking maximum estimated values in the Bellman operation. Its variants in the deep Q-learning paradigm...
Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization
Standard dynamics models for continuous control make use of feedforward computation to predict the conditional distribution of next state and reward given current state and action using a...
Parallel Distributional Prioritized Deep Reinforcement Learning for Unmanned Aerial Vehicles
This work presents a study on parallel and distributional deep reinforcement learning applied to the mapless navigation of UAVs. For this, we developed an approach based on the Soft Actor-Critic...
Variational Latent Branching Model for Off-Policy Evaluation
Model-based methods have recently shown great potential for off-policy evaluation (OPE); offline trajectories induced by behavioral policies are fitted to transitions of Markov decision processes...
Proximal Policy Optimization via Enhanced Exploration Efficiency
Proximal policy optimization (PPO) algorithm is a deep reinforcement learning algorithm with outstanding performance, especially in continuous control tasks. But the performance of this method is...
Joint Chance Constrained Optimal Control via Linear Programming
We establish a linear programming formulation for the solution of joint chance constrained optimal control problems over finite time horizons. The joint chance constraint may represent an...
Learning Provably Stabilizing Neural Controllers for Discrete-Time Stochastic Systems
We consider the problem of learning control policies in discrete-time stochastic systems which guarantee that the system stabilizes within some specified stabilization region with probability~$1$....
A Near-Optimal Primal-Dual Method for Off-Policy Learning in CMDP
As an important framework for safe Reinforcement Learning, the Constrained Markov Decision Process (CMDP) has been extensively studied in the recent literature. However, despite the rich results...
Stability Verification in Stochastic Control Systems via Neural Network Supermartingales
We consider the problem of formally verifying almost-sure (a.s.) asymptotic stability in discrete-time nonlinear stochastic control systems. While verifying stability in deterministic control...
Fully-Decentralized MADDPG with Networked Agents
In this paper, we devise three actor-critic algorithms with decentralized training for multi-agent reinforcement learning in cooperative, adversarial, and mixed settings with continuous action...
Optimal Output Feedback Learning Control for Discrete-Time Linear Quadratic Regulation
This paper studies the linear quadratic regulation (LQR) problem of unknown discrete-time systems via dynamic output feedback learning control. In contrast to the state feedback, the optimality of...
Learning Differentiable Safety-Critical Control using Control Barrier Functions for Generalization to Novel Environments
Control barrier functions (CBFs) have become a popular tool to enforce safety of a control system. CBFs are commonly utilized in a quadratic program formulation (CBF-QP) as safety-critical...
Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning
Reinforcement Learning (RL) agents in the real world must satisfy safety constraints in addition to maximizing a reward objective. Model-based RL algorithms hold promise for reducing unsafe...
Imitation Learning with Stability and Safety Guarantees
A method is presented to learn neural network (NN) controllers with stability and safety guarantees through imitation learning (IL). Convex stability and safety conditions are derived for linear...
Safe Control with Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction methods
Learning-enabled control systems have demonstrated impressive empirical performance on challenging control problems in robotics, but this performance comes at the cost of reduced transparency and...