3. Reinforcement Learning Reference Architecture - stanlypoc/AIRA GitHub Wiki

Reinforcement Learning Reference Architecture

1. Introduction

1.1 Purpose

Standardized architectural patterns for implementing RL systems across maturity levels.

1.2 Audience

  • AI/ML Architects
  • Data Engineers
  • DevOps Teams
  • Compliance Officers

1.3 Scope & Applicability

In Scope:

  • Training/inference infrastructure
  • Agent-environment interaction patterns
  • Cloud/on-prem implementations

Out of Scope:

  • Specific algorithm implementations
  • Hardware-specific optimizations

1.4 Assumptions & Constraints

Prerequisites:

  • Python 3.8+
  • RL framework (Ray RLlib, Stable Baselines, etc.)

Technical Constraints:

  • Minimum 16GB RAM for Level 3+ systems

Ethical Boundaries:

  • Human-in-the-loop for safety-critical applications

1.6 Example Models

  • Level 2: DQN (Discrete)
  • Level 3: PPO (Continuous)
  • Level 4: Meta-RL

2. Architectural Principles

Here’s a comprehensive list of Architecture Principles for Reinforcement Learning (RL) systems, designed to guide scalable, safe, and interpretable deployments across enterprise or research settings.

🎮 2.1 Architecture Principles for Reinforcement Learning (RL)

1. Environment-Centric Design

Architect RL systems with the environment as a first-class citizen.

  • Clearly define the state, action, reward, and transition spaces.
  • Use standardized environments (OpenAI Gym, Unity ML-Agents, Isaac Gym) or domain-specific simulators (e.g., industrial control, finance).
  • Decouple policy logic from environment implementation to allow easy swapping.

2. Sample Efficiency

Optimize for minimal data requirements per learning outcome.

  • Prefer off-policy algorithms (e.g., DQN, SAC) in data-scarce settings.
  • Use experience replay, prioritized sampling, or imitation learning.
  • Pretrain with demonstrations where available (behavior cloning).

3. Scalability and Parallelization

Design for parallel experience collection and distributed training.

  • Use vectorized environments and multi-threaded agents.
  • Leverage Ray RLlib, PettingZoo, or stable-baselines3 for scalable rollouts.
  • Support multi-GPU or TPU acceleration for large-scale training.

4. Reward Engineering

Treat reward function design as a core system component.

  • Keep reward functions aligned with business objectives and safety.
  • Use reward shaping, curriculum learning, or hierarchical rewards for faster convergence.
  • Validate for reward hacking or unintended optimization behavior.

5. Policy and Value Function Modularity

Architect agents with modular policy/value structures.

  • Separate exploration vs. exploitation logic.
  • Use plug-in modules for actor, critic, or policy gradient networks.
  • Enable experimentation with different architectures: DQN, PPO, A3C, DDPG.

6. Safety and Bounded Exploration

Enforce safe exploration within physical or logical bounds.

  • Use constrained RL or shielded policies in high-risk environments.
  • Apply reward penalties for unsafe actions or boundary violations.
  • Employ early termination or fail-safes in real-world robotics or simulations.

7. Multi-Agent Coordination (if applicable)

For multi-agent RL (MARL), support communication, negotiation, and shared goals.

  • Use architectures like centralized critics or decentralized actors.
  • Track each agent’s contribution to global reward (credit assignment).
  • Address competitive vs. cooperative dynamics explicitly.

8. Observability and Diagnostics

Instrument RL systems with clear training and runtime signals.

  • Track episode returns, success rates, loss curves, and Q-value convergence.
  • Visualize agent behavior with trajectory replays or saliency maps.
  • Use TensorBoard, Weights & Biases, or MLflow for experiment logging.

9. Checkpointing & Recovery

Include robust model versioning and rollback mechanisms.

  • Checkpoint policies periodically during training.
  • Use model registries to log training metadata and artifacts.
  • Allow seamless rollback in production agents (especially in trading or robotics).

10. Transferability and Generalization

Design agents to adapt across environments and domains.

  • Use domain randomization, feature-level pretraining, or meta-RL.
  • Validate agents in unseen environments or tasks.
  • Promote reusability via embedding policies, not just raw weights.

11. Ethical and Human-in-the-Loop RL

Incorporate ethical constraints and human feedback.

  • Use preference-based RL or reward modeling from user data.
  • Penalize toxic or biased behaviors in LLM-based RLHF pipelines.
  • Make human overrides possible during real-time inference.

12. Lifecycle and Deployment Readiness

Consider training-to-inference flow in architecture.

  • Separate training pipelines (often heavy and distributed) from serving agents (lean and real-time).
  • Convert policies to deployable formats (e.g., TorchScript, ONNX).
  • Integrate with CI/CD for retraining, testing, and rollout.

2.2 Standards Compliance

Security & Privacy
Must comply with: ISO/IEC 27001
Practical tip: Encrypt replay buffers

Ethical AI
Key standards: IEEE 7000-2021
Checklist item: Bias testing in reward functions

2.3 Operational Mandates

5 Golden Rules of Agent Operations:

  1. Never overwrite production policies
  2. Maintain environment versioning
  3. Log all exploration actions
  4. Rate-limit agent decisions
  5. Manual override capability

Sample Audit Log Entry:

{
  "timestamp": "2023-07-15T14:32:11Z",
  "agent_id": "PPO-234",
  "action_hash": "a1b3f...",
  "reward": 0.87,
  "q_value": 1.23,
  "exploration_flag": true
}

3. Architecture by Technology Level

3.1 Level 2 (Basic) - Single-Agent, Discrete Actions

Definition:
Fixed action space with single decision-maker

Logical Architecture:

graph LR
    A[Environment] -->|State| B(Agent)
    B -->|Action| A
    C[Replay Buffer] -.-> B
    D[Policy Store] <--> B

Azure Implementation:

  • Compute: Azure ML Pipelines
  • Storage: Blob Store for policies

Cross-Cutting Concerns:

Area Implementation
Observability Azure Monitor + MLflow Tracking
CI/CD GitHub Actions for policy deploys

3.2 Level 3 (Advanced) - Multi-Agent, Continuous Control

Logical Architecture:

graph LR
    subgraph Environment
        A[Simulator] -->|State| B[Agent 1]
        A -->|State| C[Agent 2]
        B -->|Action| A
        C -->|Action| A
    end
    D[Central Critic] <--> B
    D <--> C
    E[Experience Pool] --> D

AWS Implementation:

  • Compute: SageMaker RL
  • Orchestration: Step Functions

Key Patterns:

  • Parameter sharing between agents
  • Centralized training with decentralized execution

3.3 Level 4 (Autonomous) - Self-Improving Systems

Logical Architecture:

graph LR
    A[Agent] -->|Meta-Gradient| B[Architecture Optimizer]
    B -->|New Config| A
    C[Environment Suite] --> A
    D[Performance Evaluator] --> B

Key Traits:

  • Automated hyperparameter tuning
  • Neural architecture search

4.0 Glossary & References

Terminology:

  • Env.: Simulation space where agents operate
  • TD Error: Temporal Difference learning metric

Related Documents:


**Visual Guide Legend:**  
```mermaid
graph TD
    square[Environment]:::env -->|State| circle(Agent):::agent
    classDef env fill:#f9f,stroke:#333
    classDef agent fill:#bbf,stroke:#f66

Pro Tip:

Always maintain a "shadow mode" deployment where agents make predictions without acting, comparing against existing systems before full deployment.