Research map / Reinforcement learning / Visual and representation RL
Fake News Mitigation via Point Process Based Intervention
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use LSTD policy iteration instead of a random policy | Fake news mitigation on synthetic networks · Synthetic networks, correlation maximization and difference minimization | total reward: roughly 20 percent | Helped |
| Use LTD rather than CEC, EXP, or RND | Fake news mitigation on the Twitter network · Twitter network, correlation maximization | correlation: 5 | Helped |
| Use LTD to choose mitigation interventions | Fake news mitigation on the Twitter network · Twitter network, difference minimization | difference in exposure: around 7 | Helped |
| Increase network density | Intervention policies on synthetic networks · Synthetic networks, correlation maximization and distance minimization | performance over random policy | Hurt |
| Increase stage length | Intervention policies on synthetic networks · Synthetic networks, correlation maximization | performance over random policy | Helped |
| Increase the number of LSTD training samples beyond 4000 | Linear value-function approximation · Initial-state value prediction | value prediction error: beyond 4000 | No clear effect |
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