Research map / Reinforcement learning / Visual and representation RL
A Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| compare PQL and GPI-LS among tabular MORL algorithms | MORL-Baselines tabular algorithms · deep-sea-treasure-v0 | maximum utility loss and Inverted Generational Distance | No clear effect |
| use GPI-PD instead of other discrete-action MORL baselines | MORL-Baselines · minecart-v0 | sample efficiency and asymptotic performance on all metrics | Helped |
| PCN versus Envelope algorithm | PCN · minecart-v0 | performance | Helped |
| GPI-based algorithms versus PGMORL and CAPQL on hypervolume and expected utility | GPI-LS and GPI-PD · mo-halfcheetah-v4 and mo-hopper-2d-v4 | hypervolume, expected utility | Helped |
| PGMORL and CAPQL versus GPI-based algorithms on sparsity | PGMORL and CAPQL · mo-halfcheetah-v4 | sparsity | Helped |
| measure by training time instead of samples | PGMORL · mo-hopper-2d-v4 | performance vs time | Mixed |
Closest papers by meaning
- On Generalization Across Environments In Multi-Objective Reinforcement Learning (ICLR 2025 · similarity 0.93)
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- An Analytical Study of Utility Functions in Multi-Objective Reinforcement Learning (NeurIPS 2024 · similarity 0.91)
- Multi-objective Linear Reinforcement Learning with Lexicographic Rewards (ICML 2025 · similarity 0.90)
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