Fish Classification Reinforcement Learning Project predict fish presence.

Work by: MUZAMIL ASLAM GANAIEE Visit: muzamilaslam.in

Overview

This project employs Reinforcement Learning techniques to classify fish based on environmental conditions. It utilizes Q-Learning within a Gym environment to predict fish presence based on specified criteria.

Reinforcement Learning Overview:

1. Problem Description and Environment Setup: The problem revolves around classifying fish conditions based on various environmental factors. The dataset is preprocessed, and conditions are determined to label fish as present or absent based on certain criteria.

2. State and Action Spaces: The environment ClassificationEnv is created using OpenAI Gym, defining the state and action spaces for the RL agent. The state space is defined as a Box space, and the action space is Discrete.

3. Q-Learning Algorithm: DQNAgent implements a Q-learning approach using a Deep Q-Network (DQN). The Q-network (_build_model) is a simple neural network with a few dense layers. It uses experience replay (remember and replay) to store and sample experiences to improve learning efficiency. The Q-learning update (learn method) computes Q-value updates based on the Bellman equation.

4. Training Loop: The RL agent interacts with the environment for a specified number of episodes (n_episodes). In each episode, the agent takes actions based on the current state, updates its Q-values, and moves to the next state. During training, it follows an epsilon-greedy policy, balancing exploration and exploitation (epsilon and epsilon_decay).

5. Testing and Evaluation: After training, the agent's performance is evaluated on a separate test set. Predicted actions on the test set are compared against the ground truth labels to compute accuracy (accuracy_score).

6. Further Improvement Strategies: Hyperparameter tuning, adjusting the neural network architecture, optimizing learning rates, and exploring different RL algorithms like SARSA, DDPG, or PPO might further enhance performance.

7. Documentation and Comments: The code contains comments to explain important functionalities and logic. Additional Resources: OpenAI Gym Documentation: Provides detailed information on creating environments and defining agents: OpenAI Gym Reinforcement Learning - Sutton & Barto: Classic textbook on RL concepts and algorithms: Reinforcement Learning: An Introduction Deep Q-Networks (DQN): Understanding and implementing DQN algorithms: Playing Atari with Deep Reinforcement Learning Deep RL Frameworks: Libraries like TensorFlow or PyTorch offer extensive RL toolkits and resources: TensorFlow, PyTorch These resources can provide in-depth insights into RL concepts and algorithms, helping expand your understanding and improve the implementation.

Components

Data Preprocessing

Dataset Loading: Reads the dataset from 'dataset.csv'. Feature Engineering: Defines conditions for fish presence based on environmental parameters. Encoding and Scaling: Encodes categorical data and scales features using StandardScaler. Train-Test Split: Splits the dataset into training and testing sets.

Q-Learning Agent

QLearningAgent: Implements a Q-Learning agent for fish classification. __init__: Initializes the Q-table, learning rate, discount factor, and exploration rate. choose_action: Chooses an action based on exploration or exploitation. learn: Updates Q-values based on the reward and next state. decay_epsilon: Reduces the exploration rate over time.

Gym Environment

ClassificationEnv: Defines the Gym environment for fish classification. __init__: Initializes the environment with data and labels. reset: Resets the environment to the initial state. step: Executes an action and provides the next state, reward, and termination signal. render: Provides rendering logic if required.

Training Loop

Main Loop: Iterates through episodes to train the Q-Learning agent.

• Chooses actions, updates Q-values, and manages the environment.
• Tracks and prints episode-wise information like scores and epsilon values.
• Evaluates the model's accuracy on the test set after training.

Libraries Used

Pandas: For data manipulation and preprocessing. NumPy: For numerical operations and array handling. Scikit-learn: For train-test split, accuracy calculation, and label encoding. Gym: For defining the reinforcement learning environment. TensorFlow Keras: For neural network model implementation. Klib: For additional data analysis and visualization.

Acknowledgments

This project uses Q-Learning for reinforcement learning, focusing on environmental conditions to predict fish presence.

References

Gym Documentation: OpenAI Gym TensorFlow Keras Documentation: TensorFlow Keras Klib Documentation: Klib