Reinforcement learning for autonomous driving
The challenge posed by autonomous driving cannot be solved by a full supervised learning approach owing to strong interactions with the environment and multiple obstacles and maneuvers (discussed previously) in the environment. The reward mechanism of reinforcement learning has to be highly effective so that the agent is very cautious about the safety of the individual inside and all the obstacles outside, whether it's humans, animals, or any ongoing construction.
One of the approaches to rewards could be:
- Agent vehicle collides with the vehicle in front: High negative reward
- Agent vehicle maintains safer distance from both front and rear end: Positive reward
- Agent vehicle maintains unsafe distance: Moderate negative reward
- Agent vehicle is closing the distance: Negative reward
- Agent vehicle speeds up: Decreasing the positive reward as the speed increases and negative when it crosses the speed limit
Incorporating recurrent neural networks (RNNs) to...
