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A Risk-Sensitive Approach to Policy Optimization

AAAI 2023 · Reinforcement learning › Policy gradient and actor-critic · approach: Regularized scalable deep value learning · 5 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use pessimistic Wang weighting (η=0.5) instead of the risk-neutral expected-reward objective (ablation)Risk-sensitive policy optimization · PointGoal2, CarButton2, PointPush2Expected Reward: 15.6 ± 0.3 -> 17.9 ± 0.4; 11.5 ± 1.2 -> 16.7 ± 0.5; 4.1 ± 0.6 -> 9.3 ± 2.1Helped
Use pessimistic Wang weighting (η=0.5) instead of risk-neutral training (ablation)Risk-sensitive policy optimization · PointGoal2, CarButton2, PointPush2CPT Value: 3.7 ± 0.3 -> 5.7 ± 0.5; 0.5 ± 1.5 -> 5.2 ± 0.8; 1.0 ± 0.5 -> 4.5 ± 1.3Helped
Use pessimistic rather than optimistic weighting (ablation)Risk-sensitive policy optimization · PointGoal2, CarButton2, PointPush2Expected Reward: 11.0 ± 3.2 -> 17.9 ± 0.4; 8.7 ± 1.0 -> 16.7 ± 0.5; 3.2 ± 2.1 -> 9.3 ± 2.1Helped
Add utility-to-go and a neural-network baseline to the base risk-sensitive gradient (ablation)CPT value optimization · Safety Gym variance-reduction experimentsSample efficiencyHelped
Add generalized advantage estimation after utility-to-go and a neural-network baseline (ablation)CPT value optimization · Safety Gym variance-reduction experimentsSample efficiencyHelped
Add trust-region clipping after generalized advantage estimation (ablation)CPT value optimization · Safety Gym variance-reduction experimentsSample efficiencyHelped
Use the paper's clipping form rather than PPO's clipping form (ablation)Risk-sensitive policy optimization · Figures 3 and 4 environmentsPerformanceHelped
Increase pessimism (η) in the risk profile (ablation)Risk-sensitive policy optimization · PointButton2 and trends across Safety Gym environmentsAccumulated rewardMixed
Increase pessimism (η) in the risk profile (ablation)Risk-sensitive policy optimization · PointButton2 and constrained Safety Gym environmentsCostHelped

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