Reinforcement Learning
Off-policy Reinforcement Learning with Model-based Exploration Augmentation
This paper introduces Modelic Generative Exploration (MoGE), a novel off-policy Reinforcement Learning (RL) framework designed to enhance exploration by generating under-explored critical states and synthesizing dynamics-consistent experiences through transition models. MoGE addresses the limitations of traditional active and passive exploration methods, which struggle in high-dimensional environments or suffer from limited sample diversity. It comprises a diffusion-based generator for critical states, guided by a utility function, and a one-step imagination world model for constructing critical transitions. MoGE's modular design allows seamless integration with existing off-policy algorithms, demonstrating remarkable gains in sample efficiency and performance across complex control tasks on OpenAI Gym and DeepMind Control Suite benchmarks. The framework is shown to effectively bridge exploration and policy learning, offering a robust approach to overcoming challenges in diverse RL applications.
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The Challenge: Limited Exploration in RL
Existing RL exploration methods, both active (policy-driven stochasticity) and passive (replay buffer prioritization), face significant limitations. Active exploration struggles in high-dimensional spaces and with limited interaction trajectories, leading to unexplored critical regions. Passive exploration, while reusing valuable experiences, is confined to previously collected samples and lacks diversity, often remaining biased towards the original data distribution. This restricts policy robustness and optimal learning.
MoGE: A Novel Exploration Paradigm
MoGE proposes a novel exploration paradigm that generates critical transitions across the entire state space, guided by exploratory priors. It leverages a conditional diffusion generator to synthesize high-exploratory states and a one-step imagination world model to simulate dynamics-consistent transitions. By aligning the generator's distribution with the optimal policy's occupancy measure and ensuring dynamic consistency, MoGE provides novel, valid, and policy-improving samples. This modular framework integrates seamlessly with off-policy RL algorithms to enhance exploration and accelerate learning.
How MoGE Works: Generator & World Model
MoGE is built upon two core components: (1) a conditional diffusion generator that synthesizes critical states based on a utility function (policy entropy or TD error), and (2) a one-step imagination world model that predicts next states and rewards for arbitrary (s,a) pairs. The generator's training distribution is aligned with the replay buffer's stationary occupancy measure, ensuring state-space compliance. The world model, pre-trained via supervised learning, ensures dynamic consistency for generated transitions. An importance sampling method is used to mix generated critical transitions with replay buffer samples for policy improvement and evaluation, without altering the core structure of existing off-policy RL algorithms.
Proven Performance Across Benchmarks
Empirical results on OpenAI Gym and DeepMind Control Suite benchmarks demonstrate MoGE's superior performance in both sample efficiency and final performance. For instance, in DMC tasks, MoGE achieved an average Total Average Return (TAR) of 817.7, a +43.8% improvement over DSAC (568.5). In Humanoid-walk, it showed a +508.6% improvement over DSAC. In OpenAI Gym, MoGE achieved an average TAR of 9135.5, a +10.0% increase over standard DSAC (8301.0). Ablation studies confirm the importance of utility function choice, guidance scale, and mix ratio for optimal performance.
Driving Enterprise AI Forward
MoGE offers significant business value by accelerating the development and deployment of robust AI systems in complex, high-dimensional environments. Its ability to efficiently explore vast state spaces directly translates to faster training times, reduced computational costs, and improved performance for applications such as autonomous driving, robotics, and large language models. This leads to quicker time-to-market for AI-powered products and services, higher reliability in operational settings, and a competitive advantage through more capable AI agents. However, careful validation is needed to prevent overconfidence in simulation-trained policies.
Key Performance Metric
43.8% Average TAR Improvement (DMC Suite)Enterprise Process Flow
Collect Samples (Replay Buffer)
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Policy & Value Network
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Critical State Generation (Diffusion Model)
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One-step Imagination (World Model)
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Augmented Replay Buffer
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Policy Improvement & Evaluation
| Feature | MoGE | Traditional Active Exploration | Traditional Passive Exploration |
|---|---|---|---|
| Sample Diversity | Generates novel, high-utility states beyond observed data | Limited by actual interacted trajectories | Confined to previously collected samples, often biased |
| High-Dimensional Performance | Robust and efficient, guided by policy-relevant priors | Struggles with scalability and critical region discovery | Limited by sample availability and state-space coverage |
| Dynamic Consistency | Ensured by one-step imagination world model | Implicit, relies on environment interaction | No explicit mechanism for generated samples |
Case Study: Humanoid-walk Performance Enhancement
Challenge: Controlling high-dimensional humanoid robots to walk at target velocities while maintaining balance is a complex RL task, often suffering from sparse rewards and difficult exploration.
Approach: MoGE was integrated with standard off-policy RL algorithms (DSAC, TD3) to generate critical states and dynamically consistent transitions relevant to humanoid locomotion. The diffusion generator synthesized states with high policy entropy or TD error, while the world model provided accurate next-state predictions.
Outcome: MoGE achieved a remarkable +508.6% improvement in Total Average Return over the original DSAC in the Humanoid-walk task, reaching 891.7 compared to 146.5. This demonstrates MoGE's ability to significantly boost performance and sample efficiency in highly complex, high-dimensional control problems.
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Your AI Implementation Roadmap
A structured approach to integrating cutting-edge RL exploration into your enterprise.
Phase 1: Discovery & Strategy
Assess current systems, identify high-impact RL applications, and define clear objectives and success metrics for MoGE integration.
Phase 2: Pilot & Customization
Develop a MoGE-enhanced RL pilot project, customize the generative exploration models to your specific environment dynamics and data.
Phase 3: Integration & Optimization
Seamlessly integrate MoGE with existing off-policy RL algorithms. Implement robust monitoring and iterative optimization for peak performance.
Phase 4: Scalability & Expansion
Scale the solution across enterprise-wide applications, ensuring long-term adaptability and continuous improvement.
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