PWM: Policy Learning with Large World Models (2407.02466v2)

Abstract: Reinforcement Learning (RL) has achieved impressive results on complex tasks but struggles in multi-task settings with different embodiments. World models offer scalability by learning a simulation of the environment, yet they often rely on inefficient gradient-free optimization methods. We introduce Policy learning with large World Models (PWM), a novel model-based RL algorithm that learns continuous control policies from large multi-task world models. By pre-training the world model on offline data and using it for first-order gradient policy learning, PWM effectively solves tasks with up to 152 action dimensions and outperforms methods using ground-truth dynamics. Additionally, PWM scales to an 80-task setting, achieving up to 27% higher rewards than existing baselines without the need for expensive online planning. Visualizations and code available at https://www.imgeorgiev.com/pwm

• The paper introduces PWM, an RL algorithm that employs differentiable simulators and first-order gradient methods to manage high-dimensional action spaces.
• It reveals that well-regularized world models, rather than merely accurate ones, enhance policy learning by ensuring smoother gradients and minimizing the optimality gap.
• PWM achieves up to 27% higher rewards in multi-task scenarios without expensive online planning, significantly boosting scalability and efficiency.

Policy Learning with Large World Models: A Summary

The paper "PWM: Policy Learning with Large World Models" introduces a novel reinforcement learning (RL) algorithm called Policy Learning with Large World Models (PWM), aimed at overcoming scalability and efficiency challenges in multi-task settings.

Core Contributions

The paper presents three core contributions:

1. Efficient Policy Learning: PWM innovates by using pre-trained large world models as differentiable simulators, leveraging their smoothness and gradient stability. Unlike traditional RL methods that rely on zeroth-order optimization, PWM employs first-order gradient (FoG) methods. Empirically, this approach solves tasks with up to 152 action dimensions, significantly outperforming methods that rely on ground-truth dynamics.
2. Improving Policy Learning from World Models: The paper demonstrates that higher accuracy world models do not necessarily lead to better policies. Instead, the efficacy of a world model for policy learning is shown to depend on its smoothness, optimality gap, and stable gradients over long horizons. These insights inform the design of PWM, which prioritizes regularized world models over merely accurate ones.
3. Scalability in Multi-Task Settings: PWM exhibits superior performance in a setting of up to 80 tasks, achieving up to 27% higher rewards than previous baselines. This is achieved without resorting to expensive online planning, emphasizing the efficiency of the PWM framework.

Methodology

PWM adopts a model-based RL approach, harnessing large multi-task world models for efficient policy training. Here's an overview of the methodology:

1. World Model Pre-Training: The world model is pre-trained on offline data to learn environment dynamics and reward functions. This decouples the model learning phase from the policy learning phase.
2. First-Order Gradient Optimization: For policy training, PWM uses first-order gradients derived from the pre-trained world model. This method reduces gradient variance and improves optimization efficiency, particularly in non-smooth, contact-rich environments. The paper provides empirical evidence that well-regularized world models mitigate the gradient variance typically seen in chaotic systems.
3. Algorithm Structure: The PWM algorithm includes a learned encoder, dynamics model, and reward model, fine-tuned to minimize sample error and optimize policy learning. During policy training, PWM rolls out multiple trajectories in parallel, employing TD($\lambda$) for critic optimization and FoG for actor optimization.

Empirical Evaluation

The empirical results underscore the effectiveness of PWM:

• Single-Task Environments:

PWM was evaluated on complex locomotion tasks, such as Hopper, Ant, Anymal, Humanoid, and SNU Humanoid, and compared against SHAC, TD-MPC2, PPO, and SAC. PWM consistently achieved higher rewards, indicating the advantage of using regularized world models and FoG optimization.

• Multi-Task Scenarios:

In a composite evaluation of 30 and 80 task settings, including dm_control and MetaWorld tasks, PWM outperformed TD-MPC2 by 27% in reward without needing online planning. The paper also demonstrated that PWM could match the performance of single-task experts (SAC, DreamerV3) with significantly lower training times per task.

Theoretical and Practical Implications

The proposed PWM framework has several implications:

• Improved Scalability:

By decoupling world model training from policy learning and utilizing first-order gradients, PWM scales efficiently to large multi-task settings, crucial for deploying RL in real-world applications.

• Enhanced Efficiency:

The computational efficiency of PWM, particularly the reduced inference time compared to methods relying on online planning, is significant for scenarios requiring real-time decision-making.

• Refocusing World Model Design:

The finding that regularized world models are more beneficial for policy learning than highly accurate ones may shift the focus in world model research towards designs that enhance gradient stability and minimize the optimality gap.

Future Research Directions

Future research could explore several extensions and refinements:

1. Image-Based Inputs: Extending PWM to handle high-dimensional inputs, such as images, could broaden its applicability to vision-based tasks.
2. Efficient World Model Architectures: Investigating architectures that balance training efficiency and policy optimality, potentially through non-autoregressive models, could further enhance PWM's performance.
3. Real-World Applications: Applying PWM to real-world robotics and control systems could test its practical utility and uncover additional challenges and opportunities.

Conclusion

PWM represents a significant advancement in reinforcement learning, strategically leveraging large world models for efficient, scalable policy learning. By emphasizing gradient stability and regularization, PWM challenges current paradigms and sets the stage for more robust and applicable RL methodologies in multi-task settings.