This document summarizes a student's seminar presentation on using deep reinforcement learning for intelligent traffic light control. It notes that India loses $22 billion annually to traffic congestion, which causes economic losses and increased accidents. As populations grow, transportation demands increase traffic issues. The student proposes using deep reinforcement learning to dynamically adjust traffic light durations based on real-time road conditions from cameras, with the goal of minimizing wait times at intersections. The model would use number of cars at each direction as the state, light duration changes as actions, and cars passing per cars waiting as the reward to optimize via Q-learning.

1. “A Deep Reinforcement
Learning Network for
Traffic Light Cycle Control”
• Name:-Priti jha
• Roll no:-3434
• Branch:-Computer B
• C Number:-C22017111041
• Seminar guide:- Hitendra Khairnar

2. Motivation
1)India looses 1.5 lakh crore due
to traffic congestion every year
(business today)
2)Traffic congestion has significant
effect on economy due to wastage of
fuels,time and noise pollution.
3)According to worldbank.org Road
deaths and injuries are mainly due to
waiting vehicles in the queue on the road..
4)With the growing population,the
demand for mode of transportation also
increases which affects the traffic
problem.Due to this death rate also
increases.

3. Solution:
• Making smart self driving vehicles which can communicate with each
other to control the traffic flow in the city.(It's not yet build)
• Making an intelligent system to dynamically change the duration of
traffic lights in the city in order to integrate the system and manage
the congestion. (This majorly helps at intersection which is required
to make the whole system intelligent)

4. 4 Phases of Traffic Light(How Long each phase
should last?):

5. Traditionally the solution was based on the loop sensors that sense the vehicle
passing whereas the new solution is based on the survellience
cameras which takes the nearby road condition into consideration.

6. Manually controlled methods for traffic light control
• Scats and Scoots were the earlier manual models.

7. Automatic methods for traffic light cycle control

8. Reinforcement learning
Objective:- Minimize waiting time at intersection
State:-Number of cars at each direction(N,S,E,W)
Action:-Modify traffic light duration.
Reward:-(number of cars passing intersection) / (number of cars at intersection)

9. Reinforcement learning
• A reinforcement learning for Traffic control model is defined as
<S,A,R,T>.
• S : the possible state space. s is a specific state (s ∈ S);
• A : the possible action space. a is an action (a ∈ A);
• R : the reward space. rs,a denotes the reward in taking action a at state s;
• T : the transition function space among all states,
• The equation of Q in reinforcement learning uses ᴨ value for
optimisation of rewards.

10. Deep Q Network

11. How deep reinforcement learning works?

12. Thank you!!!!!!!!!!!!!!!!!