Objective

We aim to experiment with reinforcement learning, incorporating LSTM networks, utilizing stock data as the primary dataset.

Background

Reinforcement learning has shown promising results in various domains, and integrating it with LSTM for time-series data like stock prices/trades could be insightful. The intent is to create an AI agent that can interact with the time series of trades, make decisions, and learn from its actions.

Task

1. Project Setup

• Begin by setting up a new project or branch based on the Gymnasium framework. Reference: Gymnasium GitHub Repository.

2. Data Preparation

• Incorporate time-series stock data, ensuring it's properly cleaned and structured.
• Identify features that will be fed into the LSTM network.
• Ensure a clear distinction between training and testing datasets.

3. Gym Environment Creation

• Develop a custom gym environment tailored for our stock trading scenario.
• The environment should be able to provide the agent with relevant state representations from the stock data.
• The agent should receive rewards/penalties based on its actions and their respective outcomes.

4. AI Agent Integration

• Implement an AI agent that will be called by the gym environment.
• Incorporate LSTM networks for processing sequences of stock trades.
• Ensure the agent is capable of learning from its actions using reinforcement learning techniques.

5. Simulator

• Develop a basic simulator that will iterate through the time series of trades.
• The simulator should interact with the AI agent, allowing it to make decisions at each time step.
• Monitor and record the performance of the agent throughout the simulation.

Outcome

By the end of this task, we expect to have a basic prototype that allows us to test and observe the behavior of an LSTM-based reinforcement learning agent in a stock trading scenario.

Next Steps

Post completion of this basic layout, we will:

• Analyze the performance of the AI agent.
• Optimize the model and refine the environment.
• Plan further experiments based on initial findings.

Additional Notes

• Collaborators and contributors are encouraged to share relevant resources or research that could aid in this experimentation.
• Ensure documentation is maintained at every step for clarity and future reference.