Research map / Reinforcement learning / Policy gradient and actor-critic
Trust Region-Based Safe Distributional Reinforcement Learning for Multiple Constraints
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use risk-averse constraints (α=0.25) with SDAC | SDAC · Safety Gym tasks | total constraint violations: 1.78 times fewer total CVs than the second-best algorithm | Helped |
| Use gradient integration to handle multiple constraints concurrently | SDAC · Locomotion tasks | steps to satisfy all constraints: 1.93 times fewer than the second-best algorithm on average | Helped |
| Increase the TD(λ) trace-decay value (ablation) | SDAC distributional critics · Safety Gym tasks | constraint violations | Mixed |
| Use gradient integration instead of minimizing constraints sequentially (ablation) | SDAC feasibility handling · Cassie task | steps to reach feasibility | Helped |
| Use gradient integration instead of minimizing constraints sequentially (ablation) | SDAC feasibility handling · Cassie task | training stability | Helped |
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