Research map / Reinforcement learning / Visual and representation RL
Efficient Macroscopic Urban Traffic Models for Reducing Congestion: A PDDL+ Planning Approach
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use PDDL+ planning with UTC heuristic instead of fixed-time control | urban traffic light control · Scenario A | time steps to goal: 78 – 82 -> 25 | Helped |
| use PDDL+ planning instead of best reactive threshold | urban traffic light control · Scenario B | time steps to goal: 4650 – 6286 -> 3842 | Helped |
| use PDDL+ planning instead of reactive control | urban traffic light control · Scenario B.1 and B.2 (not yet congested) | time steps to goal: B.2: 1515 – 3728 -> 686 | Helped |
| use PDDL+ planning instead of reactive control in very low congestion | urban traffic light control · Scenario B.3 | time steps to goal: 124 – 1968 -> 190 | Mixed |
| increase alpha above 0.2 in queue-threshold action precondition (ablation) | UPMurphi UTC planner · search pruning | plan quality | No clear effect |
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