Soft MPCritic: Amortized Model Predictive Value Iteration
arXiv:2604.01477v1 Announce Type: new Abstract: Reinforcement learning (RL) and model predictive control (MPC) offer complementary strengths, yet combining them at scale remains computationally challenging. We propose soft MPCritic, an RL-MPC framework that learns in (soft) value space while using sample-based planning for both online control and value target generation. soft MPCritic instantiates MPC through model predictive path integral control (MPPI) and trains a terminal Q-function with fitted value iteration, aligning the learned value function with the planner and implicitly extending the effective planning horizon. We introduce an amortized warm-start strategy that recycles planned open-loop action sequences from online observations when computing batched MPPI-based value targets. This makes soft MPCritic computationally practical, while preserving solution quality. soft MPCritic plans in a scenario-based fashion with an ensemble of dynamic models trained for next-step predict
arXiv:2604.01477v1 Announce Type: new Abstract: Reinforcement learning (RL) and model predictive control (MPC) offer complementary strengths, yet combining them at scale remains computationally challenging. We propose soft MPCritic, an RL-MPC framework that learns in (soft) value space while using sample-based planning for both online control and value target generation. soft MPCritic instantiates MPC through model predictive path integral control (MPPI) and trains a terminal Q-function with fitted value iteration, aligning the learned value function with the planner and implicitly extending the effective planning horizon. We introduce an amortized warm-start strategy that recycles planned open-loop action sequences from online observations when computing batched MPPI-based value targets. This makes soft MPCritic computationally practical, while preserving solution quality. soft MPCritic plans in a scenario-based fashion with an ensemble of dynamic models trained for next-step prediction accuracy. Together, these ingredients enable soft MPCritic to learn effectively through robust, short-horizon planning on classic and complex control tasks. These results establish soft MPCritic as a practical and scalable blueprint for synthesizing MPC policies in settings where policy extraction and direct, long-horizon planning may fail.
Executive Summary
Soft MPCritic, a novel reinforcement learning (RL) and model predictive control (MPC) framework, tackles the challenges of combining RL and MPC at scale. This framework learns in soft value space, utilizes sample-based planning for online control and value target generation, and incorporates an amortized warm-start strategy to recycle planned action sequences. By leveraging model predictive path integral control (MPPI) and fitted value iteration, soft MPCritic enables effective learning through robust, short-horizon planning on classic and complex control tasks. This approach offers a practical and scalable solution for synthesizing MPC policies in settings where traditional methods may fail. Soft MPCritic's scenario-based planning with ensemble dynamic models further enhances its robustness and adaptability. The results presented in this paper establish soft MPCritic as a promising framework for RL and MPC applications.
Key Points
- ▸ Soft MPCritic combines RL and MPC at scale through soft value space learning and sample-based planning.
- ▸ The framework incorporates model predictive path integral control (MPPI) and fitted value iteration for effective learning.
- ▸ An amortized warm-start strategy is used to recycle planned action sequences for improved performance.
Merits
Strength in Scalability
Soft MPCritic's design enables efficient and scalable learning, making it a promising solution for complex control tasks.
Robust Planning
The framework's use of sample-based planning and model predictive path integral control (MPPI) allows for robust and effective planning even in uncertain environments.
Adaptability
Soft MPCritic's scenario-based planning with ensemble dynamic models enhances its adaptability and ability to handle changing environments.
Demerits
Computational Complexity
The framework's reliance on sample-based planning and value iteration may lead to increased computational complexity, particularly for tasks with long planning horizons.
Limited Generalizability
Soft MPCritic's effectiveness may be limited to specific control tasks and environments, and its generalizability to other domains and applications is unclear.
Expert Commentary
The work presented in this paper is a significant contribution to the field of RL and MPC, as it addresses the long-standing challenge of combining these two approaches at scale. Soft MPCritic's design and implementation demonstrate a deep understanding of the underlying principles and limitations of both RL and MPC. The results presented in the paper are impressive, and the framework's scalability and adaptability make it an attractive option for real-world applications. However, further research is needed to fully explore the framework's potential and limitations. In particular, the computational complexity of the framework and its generalizability to other domains and applications require further investigation. Nonetheless, soft MPCritic is a promising framework that has the potential to revolutionize the field of RL and MPC.
Recommendations
- ✓ Further research is needed to fully explore the framework's potential and limitations, particularly in terms of computational complexity and generalizability.
- ✓ The framework's adaptability and scenario-based planning should be further investigated to fully understand its capabilities and limitations.
Sources
Original: arXiv - cs.LG
