"Constraints as Rewards: Reinforcement Learning for Robots without Reward Functions"

No more reward function headaches - just tell the robot what NOT to do.

This paper introduces a way to replace reward functions with constraint functions in reinforcement learning, eliminating manual reward tuning for robot control tasks.

-----

https://arxiv.org/abs/2501.04228

🤖 Original Problem:

Training robots using reinforcement learning requires carefully designed reward functions with multiple weighted objectives. Tuning these weights through trial-and-error is time-consuming and relies heavily on expert knowledge.

-----

🔧 Solution in this Paper:

→ The paper proposes Constraints as Rewards (CaR), which replaces reward functions with constraint functions

→ CaR automatically balances different objectives using Lagrange multipliers instead of manual weight tuning

→ Four constraint function designs are introduced: timestep probability, timestep value, episode probability, and episode value constraints

→ A new algorithm called QRSAC-Lagrangian is developed to solve reinforcement learning with constraints efficiently

-----

💡 Key Insights:

→ Constraint functions provide more intuitive task specification than reward functions

→ Automatic weight tuning eliminates the need for manual reward engineering

→ The approach works well for tasks that can be fully expressed as constraints

-----

📊 Results:

→ Successfully generated stand-up motion for a 6-wheeled robot in both simulation and real-world

→ Outperformed 5 manually designed reward functions in achieving target poses

→ Demonstrated robustness on different terrains including slopes and rough surfaces

→ Achieved faster convergence than conventional algorithms like SAC-Lagrangian and PPO-Lagrangian