Research map / Reinforcement learning / Policy gradient and actor-critic
The Value Equivalence Principle for Model-Based Reinforcement Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use value-equivalence model learning instead of maximum-likelihood estimation | Model-based RL · Four rooms and catch | policy performance | Helped |
| Use value-equivalence model learning instead of maximum-likelihood estimation | Model-based RL · Catch and cart-pole | policy performance | Helped |
| Use value-equivalence model learning instead of maximum-likelihood estimation under stricter model-capacity constraints | Model-based RL · Four rooms and catch | policy performance | Helped |
| Use approximate value-equivalent model learning instead of MLE | Restricted-model toy MDP · Three-state MDP | value-function accuracy | Helped |
| Use an exact value-equivalent model rather than the MLE model | Restricted-model toy MDP · Three-state MDP | value-function accuracy | Helped |
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