Safety-Biased Policy Optimisation: Towards Hard-Constrained Reinforcement Learning via Trust Regions

• URL: http://arxiv.org/abs/2512.23770v1
• Date: Mon, 29 Dec 2025 07:15:07 GMT
• Title: Safety-Biased Policy Optimisation: Towards Hard-Constrained Reinforcement Learning via Trust Regions
• Authors: Ankit Kanwar, Dominik Wagner, Luke Ong,
• Abstract summary: Reinforcement learning (RL) in safety-critical domains requires agents to maximise rewards while strictly adhering to safety constraints.<n>We propose Safety-Biased Trust Region Policy optimisation (SB-TRPO), a new trust-region algorithm for hard-constrained RL.
• Score: 7.419036996978718
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement learning (RL) in safety-critical domains requires agents to maximise rewards while strictly adhering to safety constraints. Existing approaches, such as Lagrangian and projection-based methods, often either fail to ensure near-zero safety violations or sacrifice reward performance in the face of hard constraints. We propose Safety-Biased Trust Region Policy Optimisation (SB-TRPO), a new trust-region algorithm for hard-constrained RL. SB-TRPO adaptively biases policy updates towards constraint satisfaction while still seeking reward improvement. Concretely, it performs trust-region updates using a convex combination of the natural policy gradients of cost and reward, ensuring a fixed fraction of optimal cost reduction at each step. We provide a theoretical guarantee of local progress towards safety, with reward improvement when gradients are suitably aligned. Experiments on standard and challenging Safety Gymnasium tasks show that SB-TRPO consistently achieves the best balance of safety and meaningful task completion compared to state-of-the-art methods.

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