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This paper proposes Soft MellowMax Actor-Critic, a regularised RL algorithm with theoretical robustness guarantees, and reports that it matches Soft Actor-Critic on MuJoCo benchmarks and transfers better to real robots under observation, environment, and dynamics perturbations. The evidence rests on a modest set of robot experiments, so readers should check how many platforms and tasks were tested before generalising. Still, it gives a concrete, testable argument that regularisation can reduce the sim-to-real gap, which matters for anyone deploying learned robot control.
Regularised RL improves sim-to-real transfer
arXiv:2610.32855v1 Announce Type: new Abstract: Regularised reinforcement learning (RL) has provable robustness guarantees that should ease the transfer of policies from simulation to robots, known as sim-to-real. Yet, weakly regularised algorithms like Proximal Policy Optimisation (PPO) remain the de facto choice in sim-to-real pipelines, while the practical benefits of regularisation for transfer remain poorly characterised. In this work, we study the sim-to-real performance of commonly used RL algorithms, with the aim of characterising the effects of regularisation on robotic performance. To that end, we derive Soft MellowMax Actor-Critic (SMM-AC), a novel regularised RL algorithm based on the soft mellowmax value function, which carries strong robustness guarantees. On MuJoCo continuous control benchmarks, SMM-AC is competitive with baselines like Soft Actor-Critic (SAC). On real robots, SMM-AC achieves higher return, while exhibiting greater robustness to perturbations in observations, the environment, and dynamics than baselines. Together, these results demonstrate that regularised RL improves real-world policy deployment through better robustness to the sim-to-real gap, and changing real-world conditions.
Abstract:Regularised reinforcement learning (RL) has provable robustness guarantees that should ease the transfer of policies from simulation to robots, known as sim-to-real. Yet, weakly regularised algorithms like Proximal Policy Optimisation (PPO) remain the de facto choice in sim-to-real pipelines, while the practical benefits of regularisation for transfer remain poorly characterised. In this work, we study the sim-to-real performance of commonly used RL algorithms, with the aim of characterising the effects of regularisation on robotic performance. To that end, we derive Soft MellowMax Actor-Critic (SMM-AC), a novel regularised RL algorithm based on the soft mellowmax value function, which carries strong robustness guarantees. On MuJoCo continuous control benchmarks, SMM-AC is competitive with baselines like Soft Actor-Critic (SAC). On real robots, SMM-AC achieves higher return, while exhibiting greater robustness to perturbations in observations, the environment, and dynamics than baselines. Together, these results demonstrate that regularised RL improves real-world policy deployment through better robustness to the sim-to-real gap, and changing real-world conditions.
9 pages of main-body content, with supporting details in the appendices. Under submission
Machine Learning (cs.LG); Robotics (cs.RO)
arXiv:2610.32855 [cs.LG]
(or arXiv:2610.32855v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.32855 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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