Research map / Reinforcement learning / Policy gradient and actor-critic
Orchestrated Value Mapping for Reinforcement Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace the logarithmic mapping with a logarithmic-linear mapping | DQN · Atari 2600, 55 games | human-normalized score versus the per-game worst of LogDQN and DQN | Helped |
| Replace the logarithmic mapping with a logarithmic-linear mapping | DQN · Atari 2600, 55 games | human-normalized score versus the per-game best of LogDQN and DQN | No clear effect |
| Use a logarithmic-linear mapping rather than the logarithmic or linear mapping | DQN · Atari 2600, 55 games | human-normalized score | Mixed |
| Use a logarithmic-linear mapping rather than the logarithmic or linear mapping | DQN · Atari 2600, 55 games | human-normalized mean score | No clear effect |
Closest papers by meaning
- The Value-Improvement Path: Towards Better Representations for Reinforcement Learning (AAAI 2021 · similarity 0.90)
- Learning values across many orders of magnitude (NeurIPS 2016 · similarity 0.89)
- Inverting the Bellman Equation: From $Q$-Values to World Models (NeurIPS 2026 · similarity 0.88)
- Penalizing Infeasible Actions and Reward Scaling in Reinforcement Learning with Offline Data (ICML 2025 · similarity 0.87)
- Enhancing Value Function Estimation through First-Order State-Action Dynamics in Offline Reinforcement Learning (ICML 2024 · similarity 0.87)
- A Perspective of Q-value Estimation on Offline-to-Online Reinforcement Learning (AAAI 2024 · similarity 0.87)
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