Research map / Reinforcement learning / Offline RL
Online Optimization for Offline Safe Reinforcement Learning
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's full text. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Frames offline safe RL as a minimax Lagrangian problem and solves it with an offline RL oracle plus a bandit (EXP3) over discretized Lagrange multipliers, using truncated TD3+BC updates and no off-policy evaluation.
Problem
Offline safe RL: learn a reward-maximizing policy from a fixed dataset under a cumulative cost constraint, especially with stringent cost thresholds, where Lagrangian methods are unstable and OPE-based methods are error-prone.
Approach
Relabel offline rewards with the current Lagrange multiplier and update the multiplier via a no-regret (EXP3 multi-armed bandit over K discrete lambda values) step, without any OPE; the practical version runs only M gradient steps of the offline RL learner per round and returns the last-iterate policy.
Key results
- With cost threshold 5, O3SRL is the only method that satisfies the normalized cost constraint on all eight DSRL tasks (e.g., BallRun reward 0.25±0.03 at cost 0.00 vs CAPS 0.07±0.05).
- On BallCircle, O3SRL reaches reward 0.62-0.65 while staying safe, more than double CAPS (0.23) and FISOR (0.27).
- Increasing arms K from 2 to 5 improves rewards; K=10 gives only marginal gains over K=5.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use O3SRL instead of the safe baseline methods | Offline safe RL · DSRL tasks, cost threshold κ=5 | constraint satisfaction across tasks | Helped |
| Increase arm count from K=2 to K=5 (ablation) | O3SRL · BallRun | normalized reward: 0.20 -> 0.25 | Helped |
| Increase arm count from K=2 to K=5 (ablation) | O3SRL · AntRun | normalized reward: 0.24 -> 0.33 | Helped |
| Increase arm count from K=5 to K=10 (ablation) | O3SRL · CarRun | normalized reward: 0.96 -> 0.96 | No clear effect |
| Increase the maximum lambda value from C=2 to C=5 (ablation) | O3SRL · DroneCircle | normalized cost: 13.32 ± 2.62 -> 0.23 ± 0.34 | Helped |
| Increase the maximum lambda value from C=2 to C=5 (ablation) | O3SRL · DroneCircle | normalized reward: 0.79 ± 0.06 -> 0.49 ± 0.07 | Hurt |
| Use an adaptive rather than uniform lambda grid (ablation) | O3SRL · DroneCircle, κ=40 | normalized reward: 0.45 ± 0.03 -> 0.65 ± 0.11 | Helped |
| Use an adaptive rather than uniform lambda grid (ablation) | O3SRL · BallRun, κ=40 | normalized reward: 0.25 ± 0.01 -> 0.53 ± 0.48 | Mixed |
| Increase offline RL updates per lambda update from M=10 to M=100 (ablation) | O3SRL · DroneCircle | normalized cost: 0.23 ± 0.34 -> 4.87 ± 7.77 | Hurt |
| Replace TD3+BC with IQL as the base offline RL algorithm (ablation) | O3SRL · Circle tasks | normalized reward | Mixed |
Surprising findings
- Several baselines that achieve higher rewards (e.g., CDT on CarRun, CCAC on BallRun) do so by violating the cost constraint.
- A fixed-lambda offline RL alternative either ignores costs or sacrifices reward depending on the lambda chosen.
Limitations
- Theoretical guarantee for the practical version has rate sqrt(K/T)+1/K, slower than FTRL-based continuous no-regret rates (1/sqrt(T)).
- Reported results use a single stringent cost threshold in the main table; some tasks (e.g., AntRun, DroneRun) still show cost near or above threshold with high variance.
Experimental setup
Datasets: DSRL Bullet benchmark datasets Gymnasium Safety Suite (SafetyGym) datasets
Benchmarks: DSRL BallRun CarRun AntRun DroneRun BallCircle CarCircle AntCircle DroneCircle SafetyGym PointCircle1/2, AntVelocity, HalfCheetah, HopperVelocity, SwimmerVelocity, Walker2dVelocity
Metrics: normalized reward normalized cost reward/cost trade-off across cost limits
Baselines: BC-Safe BEAR-Lag CPQ COptiDICE CDT CCAC CAPS FISOR
Builds on: TD3+BC EXP3 BEAR-Lag FISOR
Closest papers by meaning
- Constraint-Adaptive Policy Switching for Offline Safe Reinforcement Learning (AAAI 2025 · similarity 0.93)
- Offline Safe Reinforcement Learning Using Trajectory Classification (AAAI 2025 · similarity 0.91)
- Worst-Case Offline Reinforcement Learning with Arbitrary Data Support (NeurIPS 2024 · similarity 0.91)
- Bayesian Design Principles for Offline-to-Online Reinforcement Learning (ICML 2024 · similarity 0.91)
- A Clean Slate for Offline Reinforcement Learning (NeurIPS 2025 · similarity 0.91)
- Latent Safety-Constrained Policy Approach for Safe Offline Reinforcement Learning (ICLR 2025 · similarity 0.91)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.