Research map / Reinforcement learning / Policy gradient and actor-critic
Beyond the One-Step Greedy Approach in Reinforcement Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use a nontrivial lookahead horizon h | h-PI · Toy grid-world | simulator calls | Helped |
| Use an intermediate κ value | κ-PI · Toy grid-world | simulator calls | Helped |
| Use h-PI or κ-PI instead of λ-PI | Policy Iteration · Toy grid-world | simulator calls | Helped |
Closest papers by meaning
- Generalizable Policy Improvement via Reinforcement Sampling (Student Abstract) (AAAI 2024 · similarity 0.88)
- Efficient Multi-Policy Evaluation for Reinforcement Learning (AAAI 2025 · similarity 0.88)
- On the Second-Order Convergence of Biased Policy Gradient Algorithms (ICML 2024 · similarity 0.87)
- Policy Mirror Descent with Lookahead (NeurIPS 2024 · similarity 0.87)
- Policy Gradient Algorithms in Average-Reward Multichain MDPs (NeurIPS 2026 · similarity 0.87)
- Revisiting Value Iteration: Unified Analysis of Discounted and Average-Reward Cases (NeurIPS 2026 · similarity 0.87)
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