Reinforcement Learning for Resilient Rice-Energy-Water System - 25/03/2025 22:41 EDT
Budget: $30 – $250 USD
I'm looking for an expert in Reinforcement Learning and cost minimization strategies who can design and code a comprehensive RL model for a food-energy-water system. This model should prioritize ensuring system resilience under extreme drought conditions. The system is structured as a Markov Decision Process (MDP) and will be solved via the Proximal Policy Optimization (PPO) algorithm.
Key tasks:
- Minimize the total operational cost while maintaining system constraints.
- Control the use of water (irrigation), energy flow, and food (rice) processing.
- Adapt to variable external conditions such as drought, temperature, and demand.
The ideal candidate will have:
- Experience in building Reinforcement Learning environments in the Gymnasium format.
- Proficiency in implementing PPO with Stable Baselines.
- Ability to track performance metrics (e.g., plot episode vs total cost, power shortage, rice damage, total reward vs episode) in CSV or TensorBoard.
Please note that further details can be found in the additional PDF document.
Key tasks:
- Minimize the total operational cost while maintaining system constraints.
- Control the use of water (irrigation), energy flow, and food (rice) processing.
- Adapt to variable external conditions such as drought, temperature, and demand.
The ideal candidate will have:
- Experience in building Reinforcement Learning environments in the Gymnasium format.
- Proficiency in implementing PPO with Stable Baselines.
- Ability to track performance metrics (e.g., plot episode vs total cost, power shortage, rice damage, total reward vs episode) in CSV or TensorBoard.
Please note that further details can be found in the additional PDF document.