Reinforcement learning , in which an agent learns to make decisions by interacting with the environment, has
Question:
Scenario: Autonomous Self-Driving Car Through Reinforcement Learning
This problem statement outlines the task of implementing an autonomous self-driving racing car using deep Q networks. Let's break down the key components and concepts involved:
Environment and Framework:
Agent:
Learning Approach:
Observations (States):
Actions:
Rewards:
Training Data:
Output:
In summary, the problem involves using reinforcement learning, specifically deep Q networks, to train an autonomous racing car to drive on a simulated racing track. The learning process relies on the agent's interactions with the environment, where states (captured images), actions, and rewards form the basis of training the neural network. The goal is to develop a policy that allows the agent to drive autonomously and make optimal decisions while navigating the track.
Deliverables
1. | Discussion of related techniques and algorithms. |
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2. | Implementation of algorithms [Java/C++/Python], analysis, and evaluation of results of proposed algorithms |
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