How do you deal with multi-objective or conflicting rewards in RL?

Weighted Sum: Combine multiple objectives into a single reward using weighted sums. Adjusting the weights allows you to balance the importance of different objectives, but it might not handle conflicting goals well. Scalarization Techniques: Transform multiple objectives into a single objective using scalarization functions, like weighted sum, weighted product, or other mathematical formulations. This simplifies the problem but may not capture the true trade-offs between conflicting goals. Reward Shaping: Use reward shaping to guide the learning process. Add auxiliary rewards that encourage desirable behavior or discourage unwanted actions. Be cautious to avoid unintentionally introducing new conflicts or biases.

In multi-objective reinforcement learning (RL), crafting reward functions is challenging due to the diversity of goals, like balancing efficiency in smart grids or achieving speed in robotics. Scalar rewards often oversimplify, while methods like multi-objective rewards and Pareto optimization offer nuanced approaches. Techniques like hierarchical RL simplify complex tasks, and curriculum learning introduces objectives progressively. Multi-agent RL uses different agents for specific goals in complex systems. Balancing exploration with exploitation, ensuring scalability, and maintaining interpretability are crucial. These strategies enable effective navigation of the complexities in multi-objective RL scenarios.

Normalization and also knowledge of the nature of the reward/penalty are key. Some times you need to combine a risk with a reward! So you can normalize them, and subtract from a base number like 0 or 1.

In the realm of multi-objective reinforcement learning (RL), the robot chef scenario exemplifies the complexity of creating advanced reward systems. This robot must craft meals that are delicious, healthy, and cost-effective, demanding a multifaceted reward structure. It requires a balance between health, cost, and culinary creativity, with an emphasis on understanding subjective tastes. The approach involves training models for different cooking styles and dynamically adapting to changing preferences and market conditions. This case underscores the broader RL challenge of devising systems adept at handling intricate, multi-dimensional tasks.

In multi-objective optimization for reinforcement learning, the focus is on balancing conflicting objectives to find optimal policies (Pareto front). Techniques like scalarization simplify multiple objectives into one, while decomposition segments the problem into neural-network-modeled parts. Advanced methods like Pareto-front-based deep reinforcement learning enhance optimization efficiency. Hypernetworks are used to learn the entire Pareto front for effective post-training selection. These approaches are key in managing simultaneous objectives in real-world scenarios. My go-to tool as a machine learning consultant is Pymoo, offering a variety of algorithms and visualization tools for multi-objective optimization challenges.

While multi-objective approaches offer a valuable lens, policy selection and evaluation in RL are inherently complex. Beyond multiple objectives, real-world scenarios often involve: Uncertainties: Incomplete information, dynamic environments, and inherent stochasticity necessitate methods that consider these factors. Problem-specific characteristics: Different problems demand tailored approaches, considering the number of objectives, their interplay, and the agent's capabilities. Therefore, a holistic view requires acknowledging the multi-objective framework's strengths while recognizing the need for flexibility and adaptation based on the specific problem.

Remember reward shaping is an engineering id does need deep understanding of the dynamics of the system, try to learn the problem and system first!