Gradient-based Planning with World Models (2312.17227v1)

Abstract: The enduring challenge in the field of artificial intelligence has been the control of systems to achieve desired behaviours. While for systems governed by straightforward dynamics equations, methods like Linear Quadratic Regulation (LQR) have historically proven highly effective, most real-world tasks, which require a general problem-solver, demand world models with dynamics that cannot be easily described by simple equations. Consequently, these models must be learned from data using neural networks. Most model predictive control (MPC) algorithms designed for visual world models have traditionally explored gradient-free population-based optimisation methods, such as Cross Entropy and Model Predictive Path Integral (MPPI) for planning. However, we present an exploration of a gradient-based alternative that fully leverages the differentiability of the world model. In our study, we conduct a comparative analysis between our method and other MPC-based alternatives, as well as policy-based algorithms. In a sample-efficient setting, our method achieves on par or superior performance compared to the alternative approaches in most tasks. Additionally, we introduce a hybrid model that combines policy networks and gradient-based MPC, which outperforms pure policy based methods thereby holding promise for Gradient-based planning with world models in complex real-world tasks.

• The paper introduces gradient-based MPC that leverages backpropagation through learned world models to optimize action planning.
• It employs a hybrid model combining policy networks with gradient descent refinement, yielding superior performance on tasks like Cartpole Swingup.
• Experimental results demonstrate enhanced sample efficiency and scalability to high-dimensional action spaces in complex control environments.

Gradient-Based Planning with World Models

Introduction

The paper "Gradient-based Planning with World Models" (2312.17227) presents a novel approach to addressing the enduring challenge in AI: control of systems to achieve desired behaviors. Traditional methods like Linear Quadratic Regulation (LQR) rely on simple equations to describe system dynamics, which are not applicable to the complex environments typical in real-world tasks. This research proposes leveraging learned world models via neural networks, focusing specifically on gradient-based Model Predictive Control (MPC). The authors introduce a hybrid model that synergizes policy networks with gradient-based MPC, demonstrating improved performance in various tasks.

Methodology

The primary contribution of this paper is the exploration and implementation of gradient-based MPC. Traditional MPC methods often employ gradient-free optimization techniques such as Cross Entropy and Model Predictive Path Integral (MPPI) for planning. These methods, while effective, tend to be computationally intensive and do not exploit the differentiability inherent in neural network-based world models.

Gradient-Based Model Predictive Control (Grad-MPC): The approach outlined involves deriving optimal actions by back-propagating through the learned world model and performing gradient descent.

• World Model Architecture: Utilizes a Recurrent State Space Model (RSSM) to predict state transitions. This model integrates both deterministic and stochastic state components, leveraging variational inference and gradient descent for model optimization.
• Planning Mechanism: Planning is implemented by generating Gaussian-sampled action trajectories, simulating future states using the world model, and optimizing actions to maximize expected rewards through gradient descent iterations.

Figure 1: Gradient based Planning with world models.

Hybrid Model (Policy + Grad-MPC): The integration of policy networks with gradient-based MPC aims to capitalize on the memory efficiency of neural networks while mitigating the limitations of policy networks in sparse environments. Hybrid planning initializes trajectories with policy network outputs, refining them using gradient-based optimization.

Experimental Results

The experimentation focuses on testing Grad-MPC and the hybrid model in various environments from the DeepMind Control Suite. Comparative analysis against baseline methods such as Cross-Entropy and policy-based methods (including Dreamer and SAC) was conducted.

• Performance Metrics: The paper reports superior performance in sample efficiency for Grad-MPC on tasks like Cartpole Swingup, Reacher Easy, and Finger Spin, among others. The hybrid model demonstrates enhanced performance in sparse reward environments.

  Figure 2: Test Rewards of Grad-MPC in 150k env steps. These rewards are calculated over 10 test episodes across three random seeds. Dotted lines represent the performance of PlaNet and Dreamer at 100K steps.

• Scalability: Grad-MPC exhibits robustness in scaling to high-dimensional action spaces, which is often a bottleneck for gradient-free methods.

Discussion

While the gradient-based approach shows promise, it is not without limitations. The susceptibility to local minima remains a concern, especially in complex environments with diversified state distributions. The hybrid model addresses part of this challenge by combining offline planning prowess with detailed local optimization capabilities.

Future Work: Potential improvements include hierarchical reinforcement learning frameworks that decompose complex tasks into simplified sub-tasks, suitable for Grad-MPC. Additionally, further enhancements could stem from integrating robust world modeling and regularization techniques.

Figure 3: Effect of number of Grad-MPC candidates(number of sampled trajectories) on performance for each environment(150 episodes=150k environment steps) across a single seed.

Conclusion

This paper's exploration of gradient-based planning models marks a significant stride towards efficient, scalable, and generalizable AI control systems. The hybridization with policy networks offers a compelling solution to inherent challenges in model-based reinforcement learning. Future endeavors in refining these methodologies and enhancing their applicability to complex real-world scenarios could pioneer advancements in AI-driven automation.