Research map / Reinforcement learning / Policy gradient and actor-critic
Variational Policy Gradient Method for Reinforcement Learning with General Utilities
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| increase log-barrier penalty beta (0 -> 4) in constrained MDP (ablation) | Variational policy gradient with log barrier · Frozen Lake with costly states | test cost | Helped |
| increase log-barrier penalty beta (0 -> 4) in constrained MDP (ablation) | Variational policy gradient with log barrier · Frozen Lake with costly states | test reward | Hurt |
| maximize occupancy entropy with variational PG instead of uniform random policy | Frozen Lake exploration · Frozen Lake max entropy exploration | state coverage / entropy | Helped |
| more episodes for variational gradient estimator | Algorithm for policy gradient estimation · Frozen Lake | cosine similarity to true gradient | Helped |
Closest papers by meaning
- On the Global Optimality of Policy Gradient Methods in General Utility Reinforcement Learning (NeurIPS 2025 · similarity 0.92)
- Last-Iterate Global Convergence of Policy Gradients for Constrained Reinforcement Learning (NeurIPS 2024 · similarity 0.90)
- Occupancy-based Policy Gradient: Estimation, Convergence, and Optimality (NeurIPS 2024 · similarity 0.90)
- Generalizable Policy Improvement via Reinforcement Sampling (Student Abstract) (AAAI 2024 · similarity 0.89)
- On the Second-Order Convergence of Biased Policy Gradient Algorithms (ICML 2024 · similarity 0.89)
- Learning Optimal Deterministic Policies with Stochastic Policy Gradients (ICML 2024 · similarity 0.89)
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