Research map / Reinforcement learning / MDPs and planning
Model-Based Reinforcement Learning with Multinomial Logistic Function Approximation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Upper Confidence RL with an MNL transition model instead of UC-MatrixRL | RiverSwim · RiverSwim (|S|=6, H=24) | average cumulative reward: 492.64 ± 32.23 vs. 372.47 ± 18.04 | Helped |
| Use Upper Confidence RL with an MNL transition model instead of UC-MatrixRL | RiverSwim · RiverSwim (|S|=8, H=32) | average cumulative reward: 562.83 ± 44.72 vs. 334.13 ± 21.74 | Helped |
| Use Upper Confidence RL with an MNL transition model instead of UC-MatrixRL | RiverSwim · RiverSwim (|S|=10, H=40) | average cumulative reward: 948.64 ± 74.47 vs. 632.62 ± 25.22 | Helped |
| Use Upper Confidence RL with an MNL transition model instead of UC-MatrixRL | RiverSwim · RiverSwim (|S|=20, H=80) | average cumulative reward: 1777.11 ± 99.23 vs. 682.15 ± 64.41 | Helped |
Closest papers by meaning
- Randomized Exploration for Reinforcement Learning with Multinomial Logistic Function Approximation (NeurIPS 2024 · similarity 0.93)
- Provably Efficient Reinforcement Learning with Multinomial Logit Function Approximation (NeurIPS 2024 · similarity 0.90)
- Model-based RL as a Minimalist Approach to Horizon-Free and Second-Order Bounds (ICLR 2025 · similarity 0.89)
- Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games (ICML 2024 · similarity 0.89)
- A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation (NeurIPS 2024 · similarity 0.88)
- Hybrid Reinforcement Learning Breaks Sample Size Barriers In Linear MDPs (NeurIPS 2024 · similarity 0.88)
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