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 $a = \mu_{theta}\left(s\right)$.
Paper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Reinforcement Learning | 4 | 17.39% |
Reinforcement Learning (RL) | 3 | 13.04% |
Continuous Control | 2 | 8.70% |
Adversarial Attack | 1 | 4.35% |
Decoder | 1 | 4.35% |
Face Recognition | 1 | 4.35% |
Personality Generation | 1 | 4.35% |
Language Modeling | 1 | 4.35% |
Language Modelling | 1 | 4.35% |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |