This section provides an overview of the Reinforcement Learning (RL) project we are currently working on. RL is a branch of machine learning that focuses on training agents to make decisions in an environment to maximize some notion of cumulative reward.

Project Goals

The primary goal of this project is to develop an RL agent that can learn complex decision-making tasks efficiently. The agent will be trained on a simulated environment, and we aim to evaluate its performance on real-world tasks.

Methodology

  1. Environment Design: We have designed a simulated environment that mimics real-world scenarios. The environment includes various obstacles and rewards that the agent needs to navigate to achieve its goals.
  2. Agent Architecture: The agent will be based on a deep neural network that uses reinforcement learning algorithms to learn optimal policies.
  3. Training Process: We will use a combination of off-policy and on-policy algorithms to train the agent. This will allow us to balance exploration and exploitation during the learning process.

Results

So far, we have successfully trained the agent on the simulated environment. The agent has learned to navigate through the obstacles and collect rewards. The following image shows the agent's performance over time:

Reinforcement Learning Performance

Next Steps

Our next step is to evaluate the agent's performance on real-world tasks. We plan to use a dataset that represents a real-world scenario and test the agent's ability to adapt and learn in new environments.

For more information on our project, please visit our GitHub repository.