amesa-train-dev

Create a distributed reinforcement learning (RL) training platform using the 'amesa-train-dev' package. This platform will enable users to train RL agents on complex environments across multiple machines within a cluster. The application should facilitate the following functionalities:

1. **Environment Setup**: Users should be able to specify the environment in which the agent will operate. For example, it could be a classic control problem like CartPole or a more complex game environment like Atari.
2. **Agent Configuration**: Allow users to configure the type of RL agent they wish to train (e.g., DQN, PPO, A2C). The configuration should include hyperparameters such as learning rate, discount factor, and exploration strategy.
3. **Distributed Training**: Utilize the 'amesa-train-dev' package to distribute the training process across multiple nodes in a cluster. Each node should be responsible for a portion of the training load, thereby accelerating the overall training time.
4. **Monitoring and Visualization**: Implement a dashboard that allows users to monitor the progress of the training. This dashboard should display metrics such as the average reward over time, loss values, and any other relevant statistics.
5. **Checkpointing and Resuming**: Enable users to save the state of the training at regular intervals and resume from the last checkpoint if the training process is interrupted.
6. **Evaluation Mode**: After training, allow the trained agent to be evaluated on unseen data to assess its performance. Provide a mechanism to visualize the agent's behavior in the environment.
7. **User Interface**: Develop a simple web-based user interface where users can interact with the system, input their configurations, start and stop training sessions, and view the results.

The goal is to create a robust and scalable RL training platform that leverages the capabilities of 'amesa-train-dev' to handle large-scale training tasks efficiently.