Research map / Reinforcement learning / Policy gradient and actor-critic
Fuz-RL: A Fuzzy-Guided Robust Framework for Safe Reinforcement Learning under Uncertainty
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Proposes Fuz-RL, a fuzzy-measure (lambda-fuzzy, Choquet integral) robust Bellman operator that plugs into existing safe RL methods (PPOL, CUP, CPPO) to improve robustness under observation, action, and dynamics uncertainty without min-max optimization.
Problem
Safe RL policies degrade under multi-source, coupled uncertainties (observation, action, dynamics), and existing min-max or distributionally robust methods are conservative, costly, or assume independent uncertainties.
Approach
Replace expectation over perturbation levels in the Bellman update with a discrete Choquet integral under a learned lambda-fuzzy measure over K uncertainty levels, using the reward-side (descending) and cost-side (ascending, dual measure) orderings, claimed to be equivalent to a robust CMDP.
Key results
- Fuz-RL variants claim superior safety in 94.9% of cases and robust performance in 88.9% of tasks across uncertainty settings on safe-control-gym.
- Fuz-CUP on CartPole-Stab achieves 61.4% higher AvgRet and 16.7% lower AvgRisk than CUP (as stated in text).
- On IEEE-39 bus, Fuz-PPOL improves over PPOL by 11.6% return under observation noise and 17.6% lower risk.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Integrate Fuz-RL with CPPO | CPPO · Cartpole-Stab, observation uncertainty | AvgRet: 41 ± 19 -> 59 ± 25 | Helped |
| Integrate Fuz-RL with CUP | CUP · Cartpole-Stab, observation uncertainty | AvgRisk: 0.29 ± 0.11 -> 0.26 ± 0.09 | Helped |
| Integrate Fuz-RL with CPPO | CPPO · Cartpole-Track, observation uncertainty | AvgRet: 87 ± 17 -> 93 ± 10 | Helped |
| Integrate Fuz-RL with CUP | CUP · Quadrotor-Stab, observation uncertainty | AvgRisk: 0.05 ± 0.02 -> 0.05 ± 0.02 | No clear effect |
| Integrate Fuz-RL with PPOL | PPOL · IEEE-39 Bus, observation noise | AvgRet: -5456.30 -> -4822.03 | Helped |
| Integrate Fuz-RL with PPOL | PPOL · IEEE-39 Bus, observation noise | AvgRisk: 0.17 -> 0.14 | Helped |
| Integrate Fuz-RL with PPOL | PPOL · IEEE-39 Bus, action noise | AvgRet: -6357.81 -> -5789.19 | Helped |
| Integrate Fuz-RL with PPOL | PPOL · IEEE-39 Bus, dynamic noise | AvgRisk: 0.52 -> 0.47 | Helped |
| Set uncertainty level K=1 instead of an intermediate level (ablation) | Fuz-RL · Safe-control-gym tasks, observation/action/dynamics uncertainty | episodic risk | Hurt |
| Set uncertainty level K=25 instead of an intermediate level (ablation) | Fuz-RL · Safe-control-gym tasks, observation/action/dynamics uncertainty | rewards | Hurt |
| Use an intermediate uncertainty level K=5 to K=15 (ablation) | Fuz-RL · Safe-control-gym tasks, observation/action/dynamics uncertainty | rewards | Helped |
Surprising findings
- Fuz-RL does not improve every metric: e.g., Fuz-CPPO has lower AvgRet than CPPO on CartPole-Track action uncertainty, and RAMU reaches lower AvgRisk in several settings.
Limitations
- Authors state limited scalability to high-dimensional state spaces.
- Only tested on low-dimensional control tasks and Safety-Gymnasium point robots with perturbation models that are synthetic Gaussian/impulse noise.
Experimental setup
Datasets: safe-control-gym CartPole and Quadrotor tasks Safety-Gymnasium PointGoal1, PointButton1, PointCircle1, PointPush1
Benchmarks: safe-control-gym CartPole-Stab safe-control-gym CartPole-Track safe-control-gym Quadrotor-Stab safe-control-gym Quadrotor-Track Safety-Gymnasium PointGoal1 Safety-Gymnasium PointButton1 Safety-Gymnasium PointCircle1 Safety-Gymnasium PointPush1 IEEE 39-bus frequency control
Metrics: AvgRet AvgRisk
Baselines: PPOLag CUP CPPO RAMU
Builds on: PPO-Lagrangian CUP CPPO lambda-fuzzy measure Spinning Up
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