Mixtures of Experts Unlock Parameter Scaling for Deep RL

Abstract: The recent rapid progress in (self) supervised learning models is in large part predicted by empirical scaling laws: a model's performance scales proportionally to its size. Analogous scaling laws remain elusive for reinforcement learning domains, however, where increasing the parameter count of a model often hurts its final performance. In this paper, we demonstrate that incorporating Mixture-of-Expert (MoE) modules, and in particular Soft MoEs (Puigcerver et al., 2023), into value-based networks results in more parameter-scalable models, evidenced by substantial performance increases across a variety of training regimes and model sizes. This work thus provides strong empirical evidence towards developing scaling laws for reinforcement learning.

• The paper shows that incorporating Soft MoE modules significantly enhances deep RL performance and scalability beyond baseline models.
• It reveals that structured sparsity through dynamic expert routing efficiently utilizes parameters, reducing performance degradation in larger networks.
• Comparative analysis indicates that Soft MoE’s differentiable gating mechanism outperforms traditional hard gating across various RL benchmarks.

Investigating the Impact of Mixture of Experts on Deep Reinforcement Learning Through Parameter Scaling

Introduction

Deep Reinforcement Learning (RL) has achieved remarkable successes, notably in mastering complex tasks and games. However, scaling model parameters in RL has proven challenging, often resulting in diminished performance. This is contrasted with the successes observed in supervised learning domains, where larger networks generally correspond to improved performance. A significant barrier in RL has been the efficient utilization of model parameters. Recent research has begun to pivot towards innovative architectural solutions to circumvent these scaling obstacles.

Mixture of Experts in Deep RL

A promising direction explored is the incorporation of Mixture of Experts (MoE) modules within deep RL architectures. MoE introduces a dynamic routing mechanism that directs the input to the most relevant expert or experts, depending on the task at hand. This allows the model to scale in capacity while maintaining efficiency, as not all parameters are active simultaneously. This paper specifically evaluates the effectiveness of incorporating Soft Mixture of Experts (Soft MoE) in enhancing parameter scalability and overall performance of deep reinforcement learning models.

Key Findings

• Scalability and Performance: The study demonstrates that introducing Soft MoE into the model architecture leads to substantial improvements in performance, which scales positively with the increase in the number of experts and model parameters. This contrasts with the baseline models where increasing parameter count often leads to performance degradation.
• Structured Sparsity: MoEs naturally introduce a form of structured sparsity in networks by selectively activating different subsets of parameters for different inputs. This sparsity is found to contribute positively to scaling the network, wherein the models with MoE not only perform better but do so with increasing efficiency as the model size grows.
• Comparison of MoE Variants: The research explores and compares different MoE implementations and configurations. Soft MoE, with its fully differentiable gating mechanism, outperforms the traditional hard gating methods across various training regimes and configurations, indicating its superior compatibility with deep RL paradigms.
• Impact of Design Choices: The paper conducts a detailed examination of multiple design choices such as the placement of MoE modules, gating mechanisms, tokenization of inputs, and architectural variations. Notably, the soft gating mechanism and specific tokenization strategies contribute significantly to the enhanced performance observed with MoE models.
• Exploration Beyond Standard Benchmarks: Beyond standard RL benchmarks, MoEs demonstrated promising results across various training regimes, including offline RL tasks and low-data scenarios. These findings suggest the broad applicability and potential of MoE models in a wide range of RL contexts.

Theoretical and Practical Implications

• Theoretical Understanding: The observed improvements and scalability provided by MoE modules in deep RL setups offer valuable insights into the network dynamics and learning behaviors in large-scale RL models. Specifically, it suggests that structured sparsity and selective parameter activation can be beneficial for navigating the complex optimization landscapes of deep RL.
• Efficient Resource Utilization: From a practical standpoint, MoEs present an efficient approach to leveraging increasingly large models within the computational constraints of RL environments. This efficiency can enable more complex and nuanced modeling of environments and agent behaviors.

Future Directions

The encouraging results with Soft MoE modules open numerous avenues for future research, including:

• Investigating deeper the interaction between sparsity, parameter count, and learning dynamics in deep RL.
• Extending MoE models to a broader range of RL applications, including multi-agent systems and real-world tasks.
• Exploring alternative MoE architectures and routing mechanisms tailored for specific RL challenges.

Conclusion

This research provides substantial empirical evidence supporting the use of Mixture of Experts as a viable path towards scaling deep reinforcement learning models effectively. By addressing the parameter efficiency and scalability challenges, MoE modules represent a significant step forward in realizing the potential of large-scale RL models.