Research map / Reinforcement learning / Policy gradient and actor-critic
Greedy Actor-Critic: A New Conditional Cross-Entropy Method for Policy Improvement
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace Boltzmann (SAC-style) actor update with percentile-greedy CCEM actor update (GreedyAC) | actor-critic · continuous-action classic control (across-environment tuning) | aggregate normalized return | Helped |
| Replace Boltzmann actor update with CCEM actor update | actor-critic · continuous-action environments (entropy sensitivity) | sensitivity to entropy scale | Helped |
| Replace Boltzmann actor update with CCEM actor update | actor-critic · Pendulum-CA (per-environment tuning) | final return | Hurt |
| Replace Boltzmann actor update with CCEM actor update | actor-critic · Mountain Car (per-environment tuning) | final return | Helped |
| Replace Boltzmann actor update with CCEM actor update | actor-critic · Acrobot (per-environment tuning) | learning speed | Helped |
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- Generative Actor-Critic with Soft Bridge Policies (NeurIPS 2026 · similarity 0.87)
- Decoupled Policy Actor-Critic: Bridging Pessimism and Risk Awareness in Reinforcement Learning (AAAI 2025 · similarity 0.87)
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