## Project Description
This document provides an overview of a reinforcement learning project I worked on. Due to confidentiality agreements, specific details about the project's domain and client are omitted. The focus will be on the technologies used, challenges faced, and a description of the project.
The project involved developing a reinforcement learning system to optimize a specific task within a complex driving simulation environment. The system utilized computer vision techniques to process visual input data and make decisions based on the environment's state. The goal was to train an agent to navigate the environment efficiently and achieve a set of predefined objectives.
## Technologies Used
- **Tensorflow**: Deep learning framework used for implementing reinforcement learning models.
- **OpenAI Gym**: Toolkit for developing and comparing reinforcement learning algorithms.
- **Python**: Programming language used for scripting and implementation.
- **TF-Agents**: Library for building reinforcement learning agents.
- **Carla Simulator**: Simulation environment for training RL agents in a realistic driving scenario.
- **OpenCV**: Library for computer vision tasks such as image processing and object detection.
- **NumPy**: Library for numerical computations and data manipulation.
## Challenges Faced
### 1. **Complex Environment Modeling**
- **Issue**: Designing an accurate model of the environment for reinforcement learning.
- **Solution**: Utilizing OpenAI Gym to create a simulation environment that reflects the task requirements.
### 2. **Reward Signal Design**
- **Issue**: Defining a reward signal that effectively guides the learning process.
- **Solution**: Iteratively refining the reward function based on agent performance and task objectives.
### 3. **Hyperparameter Tuning**
- **Issue**: Optimizing model hyperparameters for efficient learning.
- **Solution**: Conducting systematic experiments to identify optimal hyperparameter settings.
### 4. **Exploration vs. Exploitation**
- **Issue**: Balancing exploration of new strategies with exploitation of known successful actions.
- **Solution**: Implementing exploration strategies such as epsilon-greedy and UCB to maintain a balance.
## Domain Knowledge Requirements
In addition to expertise in reinforcement learning, the project required specialized knowledge in the following areas:
- **Deep Learning**: Understanding neural network architectures and training methods for reinforcement learning.
- **Optimization Techniques**: Knowledge of optimization algorithms to fine-tune model parameters.
- **Markov Decision Processes**: Understanding the theoretical foundations of reinforcement learning and MDPs.
- **Simulation Environments**: Experience in creating realistic simulation environments for training RL agents.