Research map / Reinforcement learning / Policy gradient and actor-critic
Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the continuous-time distributional HJB update instead of discrete-time QTD | Distributional RL on the synthetic MDP · Synthetic MDP | variance estimation across states | Helped |
| Use the continuous-time distributional HJB update instead of discrete-time QTD | Distributional RL on the synthetic MDP · Synthetic MDP | mean return accuracy | Helped |
| Account for continuous-time dynamics when updating return distributions | Distributional RL · Synthetic MDP | return distribution entropy preservation | Helped |
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