Exploring Model-Based Planning with Policy Networks
This paper introduces a new model-based reinforcement learning (MBRL) algorithm termed model-based policy planning (POPLIN), which aims to improve the sample efficiency and asymptotic performance of traditional MBRL techniques that rely on model-predictive control (MPC) or online planning, particularly in complex high-dimensional environments. Traditional MBRL methods often utilize random search techniques within the action space for planning, which may become inefficient as the dimensional complexity of the task increases.
The authors propose combining policy networks with online planning to enhance sample efficiency. The key innovation of POPLIN lies in framing action planning at each time step as an optimization problem and exploring two primary methods: optimizing action sequences initialized from a policy network or optimizing directly on the parameters of the policy network. This is a departure from random search methodologies, aiming to address their limitations in scenario complexity.
The paper reports that POPLIN achieves state-of-the-art results on several MuJoCo benchmark environments, exhibiting approximately a threefold increase in sample efficiency relative to previous state-of-the-art algorithms, such as PETS, TD3, and SAC. The authors attribute this to the smoother optimization surface found in parameter space compared to action space. They demonstrate that the policy network, once distilled, could effectively be used without the need for computationally expensive MPC during test time in situations like the Cheetah environment.
Three principal contributions outlined in the paper include:
- Using policy networks to generate proposals for MPC in high-dimensional locomotion control problems with previously unknown dynamics.
- Reformulating planning as an optimization problem with neural networks using policy planning in parameter space, achieving substantial improvements over existing methods in benchmarking environments.
- Exploring policy network distillation from planned trajectories, where the distilled network competently performs in environments like Cheetah without requiring extensive online planning.
The POPLIN algorithm is delineated into two variants: model-based policy planning in action space (POPLIN-A) and model-based policy planning in parameter space (POPLIN-P). POPLIN-A uses policy networks to propose initial action sequences and refines them using CEM, while POPLIN-P proposes noise in the policy network's parameter space. The latter variant shows superior performance in complex environments due to its efficient search process over smoother optimization surfaces.
From a theoretical standpoint, this work advances the discourse on using policy networks in conjunction with model-based planning, suggesting the potential scalability of POPLIN to more intricate tasks. Practically, the implications for real-time deployment and reduced reliance on extensive computational planning are significant, particularly for applications where ENVIRONMENT dynamics are uncertain or costly to evaluate.
The authors suggest that their exploratory findings might open new avenues for incorporating richer policy network architectures and variants of optimization techniques. Future developments could explore integrating POPLIN within broader AI control systems, particularly those demanding a balance between operational efficiency and high-dimensional decision-making agility. The release of their accompanying codebase also encourages further benchmarking and refinement by the community.