Constrained Markov Decision Processes via Backward Value Functions
This addresses safety-critical applications in robotics and autonomous systems, offering a method to enforce constraints during training, but it is incremental as it builds on existing CMDP frameworks.
The paper tackles the problem of applying reinforcement learning to physical systems with hard safety or resource constraints by modeling it as a Constrained Markov Decision Process and providing a new on-policy formulation. The result is a safe policy improvement method that ensures constraints are satisfied at every step, demonstrated on safe navigation tasks and safety-constrained MuJoCo environments with deep neural networks.
Although Reinforcement Learning (RL) algorithms have found tremendous success in simulated domains, they often cannot directly be applied to physical systems, especially in cases where there are hard constraints to satisfy (e.g. on safety or resources). In standard RL, the agent is incentivized to explore any behavior as long as it maximizes rewards, but in the real world, undesired behavior can damage either the system or the agent in a way that breaks the learning process itself. In this work, we model the problem of learning with constraints as a Constrained Markov Decision Process and provide a new on-policy formulation for solving it. A key contribution of our approach is to translate cumulative cost constraints into state-based constraints. Through this, we define a safe policy improvement method which maximizes returns while ensuring that the constraints are satisfied at every step. We provide theoretical guarantees under which the agent converges while ensuring safety over the course of training. We also highlight the computational advantages of this approach. The effectiveness of our approach is demonstrated on safe navigation tasks and in safety-constrained versions of MuJoCo environments, with deep neural networks.