Research Proposal: MoE-JEPA World Models for Efficient Reinforcement Learning and Planning#

Abstract#

Current AI research emphasizes the development of sophisticated world models capable of understanding complex dynamics, particularly from video data, often leveraging self-supervised learning (SSL) for representation extraction. Predictive models in abstract spaces (like JEPA) are gaining prominence over generative ones. Simultaneously, Mixture of Experts (MoE) offers a way to scale neural network capacity efficiently. This proposal outlines a research approach combining these trends: developing an Action-Conditioned Mixture-of-Experts Joint-Embedding Predictive Architecture (MoE-JEPA) world model. This model will be pre-trained using self-supervision on large video datasets to learn robust visual representations and environment dynamics. The MoE structure will allow the model to efficiently capture diverse or multi-modal dynamics within an environment by routing inputs to specialized expert sub-networks. This sophisticated world model will then be integrated into a model-based Reinforcement Learning (RL) framework to enable efficient planning and decision-making for agents (e.g., robots) interacting with complex environments. We hypothesize that this approach will lead to more accurate world models, improved sample efficiency in RL, and better generalization across tasks compared to monolithic world models.

1. Introduction and Motivation#

Intelligent agents capable of acting autonomously in the real world require a deep understanding of how their actions influence the environment. This understanding is often encapsulated in a “world model.” Recent trends—highlighted by researchers like Yann LeCun [2]—emphasize predictive world models trained on video data using self-supervised learning. These models focus on predictions within an abstract representation space (e.g., JEPA) rather than pixel-level generation, thus learning generalizable features for downstream planning tasks.

Real-world dynamics are complex, non-stationary, and multi-modal, making it challenging for a single monolithic network to capture such diversity. Mixture of Experts (MoE) architectures, which dynamically activate specialized expert networks based on input [3][4], offer a promising solution. This proposal bridges the concepts by developing a novel Action-Conditioned MoE-JEPA world model that integrates advanced SSL techniques, efficient expert routing, and model-based RL.

2. Literature Review#

This section summarizes key literature that forms the foundation of the proposed research:

  • World Models: World models are internal representations learned by agents to simulate and predict environmental dynamics. Pioneering work by Schmidhuber and later extensions by Ha & Schmidhuber laid the groundwork for predictive models that can forecast future states based on current inputs [1].

  • Self-Supervised Learning for Vision: Self-supervised learning (SSL) has emerged as a dominant paradigm for representation learning, especially in vision. Techniques such as contrastive learning (e.g., SimCLR, MoCo) and non-contrastive methods (e.g., BYOL, SimSiam) have shown the ability to learn powerful representations from unlabeled data. JEPA (Joint-Embedding Predictive Architecture) extends these ideas by focusing on the prediction of future or masked representations in an abstract embedding space, aligning with the vision outlined by LeCun [2].

  • Mixture of Experts (MoE): MoE architectures, as introduced by Shazeer et al. and further developed by Fedus et al., leverage multiple expert networks alongside a gating mechanism to route inputs efficiently. This approach scales model capacity while keeping computational costs sub-linear, a key feature for handling multi-modal dynamics in complex environments [3][4].

  • Model-Based Reinforcement Learning (MBRL): In MBRL, an agent learns a model of the environment’s dynamics which is then used for planning optimal actions. Techniques such as Model Predictive Control (MPC) and trajectory optimization (e.g., Cross-Entropy Method) have been successfully applied to enhance sample efficiency compared to traditional model-free RL methods.

3. Proposed Approach: MoE-JEPA World Model for MBRL#

3.1 Stage 1: Self-Supervised Pre-training of the Visual Encoder (JEPA-style)#

  • Objective: Learn robust visual representations from large-scale unlabeled video data.
  • Method: Implement a Video-JEPA framework.
  • Architecture & Training:
    • Encoder (E): Maps video clips ( x ) to representations ( z = E(x) ).
    • Predictor (P): Given context ( x_{context} ), predict the target representation ( \hat{z}{target} = P(E(x{context})) ).
    • Loss: ( L_{JEPA} = | \hat{z}{target} - \text{stop_gradient}(z{target}) |^2 ) (adapting principles from BYOL/DINO).
  • Output: A robust, pre-trained visual encoder.

3.2 Stage 2: Training the Action-Conditioned MoE World Model#

  • Objective: Model the evolution of the abstract state representation ( z ) conditioned on actions ( a ) using an MoE architecture.
  • Architecture:
    • Input: ( z_t = E(x_t) ) and action ( a_t ).
    • Gating Network (G): Determines expert routing based on ( z_t ) and ( a_t ).
    • Expert Networks (Exp_i): A set of ( N ) experts that predict potential next state representations.
    • Output Combination: Weighted combination of expert predictions to form the final prediction ( \hat{z}_{t+1} ).
    • Reward Predictor (R): Predicts immediate reward ( \hat{r}_t ).
  • Training Objective:
    • Dynamics Loss: ( L_{dynamics} = | \hat{z}{t+1} - \text{stop_gradient}(E(x{t+1})) |^2 )
    • Reward Loss: ( L_{reward} = | \hat{r}_t - r_t |^2 )
    • Auxiliary MoE Loss: ( L_{aux} ) (for load balancing among experts)
    • Total Loss: ( L_{WM} = L_{dynamics} + L_{reward} + \lambda \cdot L_{aux} )
  • Output: A trained MoE-JEPA world model consisting of (G, {Exp_i}, R) along with the frozen encoder ( E ).

3.3 Stage 3: Model-Based Reinforcement Learning and Planning#

  • Objective: Leverage the learned world model for planning and policy optimization.
  • Method: Use model-based planning algorithms (e.g., MPC or CEM).
  • Process:
    • State Encoding: Convert the current state ( x_t ) into ( z_t = E(x_t) ).
    • Trajectory Simulation: Use the world model to simulate future trajectories for candidate action sequences.
    • Action Selection: Choose the action sequence that maximizes the predicted cumulative reward.
    • Execution & Update: Execute the first action, observe the outcome, and update the replay buffer for iterative training.
  • Optional Policy Distillation: Convert the planning process into a policy network using expert iteration (e.g., DAgger) or actor-critic methods.

4. Methodology and Evaluation#

Environments#

  • Simulation benchmarks with complex visual inputs and diverse dynamics (e.g., DeepMind Control Suite, Meta-World, Isaac Gym/Habitat, CARLA).

Evaluation Metrics#

  • World Model Accuracy: Open-loop prediction error (MSE in latent space ( z )).
  • RL Performance: Sample efficiency, final task success rate, and generalization to unseen variations.
  • MoE Analysis: Expert utilization (load balancing, specialization analysis).
  • Computational Cost: Training and inference time comparisons (MoE vs. monolithic models).

Baselines#

  • Model-Free RL: Algorithms such as SAC or PPO with visual inputs.
  • MBRL with Monolithic World Model: A dense network alternative.
  • MBRL with Generative World Model: Pixel-based prediction approaches.
  • MBRL without SSL Pre-training: End-to-end training of the encoder and world model.

5. Expected Outcomes and Contributions#

  • Novel Architecture: Introduction and validation of the MoE-JEPA world model.
  • Improved World Modeling: Enhanced prediction accuracy of environmental dynamics.
  • Enhanced RL Performance: Increased sample efficiency and superior performance in complex tasks.
  • Insights into MoE for Dynamics: Analysis of expert specialization and load balancing.
  • Validation of JEPA for Planning: Evidence supporting abstract predictive models for planning.

6. Potential Challenges and Mitigation Strategies#

  • Training Stability of MoE: Sensitive hyperparameters and routing strategies.
    • Mitigation: Employ auxiliary load balancing losses, appropriate learning rate scheduling, and explore alternative routing mechanisms.
  • Compounding Errors in Long-Horizon Prediction: Accumulation of errors over time.
    • Mitigation: Use short planning horizons with frequent replanning and incorporate model uncertainty.
  • Optimal Expert Configuration: Determining the number and capacity of experts.
    • Mitigation: Systematic ablation studies and dynamic expert adjustment.
  • Computational Resource Demands: High resource requirements for training.
    • Mitigation: Utilize pre-trained encoders and distributed training frameworks.
  • Domain Gap Between SSL Data and RL Tasks: Mismatch between video data and target RL dynamics.
    • Mitigation: Use domain-relevant video data and allow slight fine-tuning of the encoder during world model training.

7. Timeline (Illustrative – 24 Months)#

  • Months 1-3: Literature review, codebase setup, environment configuration, and refining JEPA implementation.
  • Months 4-6: Implement and train the Video-JEPA encoder; evaluate representation quality.
  • Months 7-12: Develop the MoE dynamics model, integrate with JEPA encoder, and perform initial evaluations.
  • Months 13-18: Integrate the world model with an MBRL planner, train the RL agent, and compare against baselines.
  • Months 19-21: Conduct in-depth analyses (e.g., expert specialization, ablation studies).
  • Months 22-24: Final experiments, write-up, and dissemination (thesis/publications).

8. Conclusion#

This research proposes an innovative integration of self-supervised predictive learning (JEPA), Mixture of Experts (MoE), and model-based Reinforcement Learning to create more capable and efficient intelligent agents. By developing an MoE-JEPA world model, we aim to enhance the modeling of complex environmental dynamics from video data, ultimately leading to improved planning and decision-making performance in RL tasks. This approach aligns with current research trajectories and has the potential to significantly advance robotics and autonomous systems.

References#

  1. Schmidhuber, H., Ha, D., & Schmidhuber, J.
    Foundational work on world models and predictive frameworks.

  2. LeCun, Y.
    Perspectives on self-supervised learning and abstract predictive modeling in vision.

  3. Shazeer, N. et al.
    Introduction of Mixture of Experts architectures for efficient scaling.

  4. Fedus, W. et al.
    Advancements in MoE techniques applied to large-scale models.

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