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Distributed Reinforcement Learning for a Traffic Engineering Application. Mark D. Pendrith DaimlerChrysler Research & Technology Center Presented by: Christina Schweikert. Distributed Reinforcement Learning for Traffic Engineering Problem. Intelligent Cruise Control System - PowerPoint PPT Presentation
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Distributed Reinforcement Learning for a TrafficEngineering Application
Mark D. PendrithDaimlerChrysler Research & Technology Center
Presented by: Christina Schweikert
Distributed Reinforcement Learning for Traffic Engineering Problem
Intelligent Cruise Control System Lane change advisory system based on
traffic patterns Optimize a group policy by maximizing
freeway utilization as shared resource Introduce 2 new algorithms (Monte
Carlo-based Piecewise Policy Iteration, Multi-Agent Distributed Q-learning) and compare their performance in this domain
Distronic Adaptive Cruise Control
Distronic Adaptive Cruise Control
Signals from radar sensor, which scans the full width of a three-lane motorway over a distance of approximately 100m and recognizes any moving vehicles ahead
Reflection of the radar impulses and the change in their frequency enables the system to calculate the correct distance and the relative speed between the vehicles
Distronic Adaptive Cruise Control
Distance to vehicle in front reduces - cruise control system immediately reduces acceleration or, if necessary, applies the brake
Distance increases – acts as conventional cruise control system and, at speeds of between 30 and 180 km/h, will maintain the desired speed as programmed
Driver is alerted of emergencies
Distronic Adaptive Cruise Control
Automatically maintains a constant distance to the vehicle in front of it, prevent rear-end collisions
Reaction time of drivers using Distronic is up to 40 per cent faster than that of those without this assistance system
Distributed Reinforcement Learning
State – agents within sensing range Agents share a partially observable
environment Goal - Integrate agents’ experiences
to learn an observation-based policy that maximizes group performance
Agents share a common policy, giving a homogeneous population of agents
Traffic Engineering Problem
Population of cars, each with a desired traveling speed, sharing a freeway network
Subpopulation with radar capability to detect relative speeds and distances of cars immediately ahead, behind, and around them
Problem Formulation
Optimize average per time-step reward, by minimizing the per-car average loss at each time step
vd(i) desired speed of car i
va(i) actual speed of car i
n number of cars in simulation at time-step
State Representation View of the world for each car represented
by 8-d feature vector – relative distances and speeds of surrounding cars
AL AC AR
CL Car CR
BL BC BR
Pattern of Cars in Front of Agent
AL AC AR
0 – lane is clear (no car in radar range or nearest car is faster than agent’s desired speed)
1 – fastest car less than desired speed
2 – slower 3 - still slower
Pattern of Cars Behind Agent
AL AC AR
0 – lane is clear (no car in radar range or nearest car is slower than agent’s current speed)
1 – slowest car faster than desired speed
2 – faster 3 - still faster
Lane Change
CL CAR CR
0 – lane change not valid 1 – lane change valid
If there is not a safe gap in front and behind, land change is illegal.
Monte Carlo-based Piecewise Policy Iteration
Performs approximate piecewise policy iteration where possible policy changes for each state are evaluated by Monte Carlo estimation
Piecewise - Policy for each state is changed one at a time, rather than in parallel
Searches the space of deterministic policies directly without representing the value function
Policy Iteration
Start with arbitrary deterministic policy for given MDP
Generate better policy by calculating best single improvement in policy possible for each state (MC)
Combine all changes to generate successor policy
Continue until no improvement is possible – optimal policy
Multi-Agent Distributed Q-Learning
Q-Learning Q-value estimates updated after each
time step based on state transition after action is selected
For each time step, only one state transition and one action used to update Q-value estimates
In DQL, there can be as many state transitions per time step as there are agents
Multi-Agent Distributed Q-Learning
Takes the average backup value for a state/action pair <s, a> over all agents that selected action a from state s at the last time step
Qmax component of backup value is calculated over actions valid for a particular agent to select at the next time-step
Simulation for Offline Learning
Advantages: o Since true state of the environment is
known, can directly measure loss metric o Can be run faster, many long learning
trials o Safety
Learn policies offline then integrate into intelligent cruise control system with lane advisory, route planning, etc.
Traffic Simulation Specifications
Circular 3 lane freeway 13.3 miles long with 200 cars
Half follow “selfish drone” policy Rest follow current learnt policy and
active exploration decisions Gaussian distribution of desired speeds,
mean of 60 mph Cars have low level collision avoidance,
differ in lane change strategy
Experimental Results
Selfish drone policy – consistent per-step reward of -11.9 (each agent traveling 11.9 below desired speed)
APPIA and DQL found policies 3-5% better Best policies with “look ahead” only “look behind” model provided more
stable learning “look behind” outperforms “look ahead”
at times when good policy is lost
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