Policy Gradient Methods
Deterministic Policy Gradient
Deterministic Policy Gradient, or DPG, is a policy gradient method for reinforcement learning. Instead of the policy function $\pi\left(.\mid{s}\right)$ being modeled as a probability distribution, DPG considers and calculates gradients for a deterministic policy $a = \mu_{theta}\left(s\right)$.
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Reinforcement Learning (RL) | 3 | 23.08% |
| Continuous Control | 2 | 15.38% |
| Adversarial Attack | 1 | 7.69% |
| Face Recognition | 1 | 7.69% |
| Personality Generation | 1 | 7.69% |
| Object Detection | 1 | 7.69% |
| Abstractive Text Summarization | 1 | 7.69% |
| Code Generation | 1 | 7.69% |
| Model Predictive Control | 1 | 7.69% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
