GRL-v0 Implementation Guide¶

Purpose: Technical specifications for implementing GRL-v0
Audience: Developers ready to implement GRL
Prerequisites: Familiarity with tutorial chapters

Overview¶

This directory provides implementation specifications for GRL-v0. Each component is documented with:

Theoretical foundation
Interface design
Implementation details
Testing strategy

Architecture Overview¶

GRL-v0 is organized into four layers spanning both Part I (Reinforcement Fields) and Part II (Emergent Structure):

┌─────────────────────────────────────────────────────────────────┐
│         Layer 4: Abstraction (Part II: Emergent Structure)      │
│  ┌─────────────────────────┐  ┌─────────────────────────────┐  │
│  │  Spectral Clustering    │  │   Concept Hierarchy         │  │
│  │  (Functional clusters)  │  │  (Multi-level abstraction)  │  │
│  └─────────────────────────┘  └─────────────────────────────┘  │
├─────────────────────────────────────────────────────────────────┤
│         Layer 3: Inference (Part I: Reinforcement Fields)       │
│  ┌─────────────────────────┐  ┌─────────────────────────────┐  │
│  │    Policy Inference     │  │   Soft State Transitions    │  │
│  │  (Energy minimization)  │  │  (Distributed successors)   │  │
│  └─────────────────────────┘  └─────────────────────────────┘  │
├─────────────────────────────────────────────────────────────────┤
│         Layer 2: Reinforcement (Part I: Reinforcement Fields)   │
│  ┌─────────────────────────┐  ┌─────────────────────────────┐  │
│  │      RF-SARSA           │  │      MemoryUpdate           │  │
│  │  (Two-layer TD system)  │  │  (Belief transition)        │  │
│  └─────────────────────────┘  └─────────────────────────────┘  │
├─────────────────────────────────────────────────────────────────┤
│         Layer 1: Representation (Part I: Reinforcement Fields)  │
│  ┌─────────────────────────┐  ┌─────────────────────────────┐  │
│  │    Particle Memory      │  │     Kernel Functions        │  │
│  │   (Belief state Ω)      │  │     (RKHS geometry)         │  │
│  └─────────────────────────┘  └─────────────────────────────┘  │
└─────────────────────────────────────────────────────────────────┘

Part I (Layers 1-3): Particle-based learning, reinforcement fields, belief-state inference

Part II (Layer 4): Emergent structure discovery, spectral concept formation, hierarchical control

Based on: Section V of the original paper (Chiu & Huber, 2022)

Implementation Specifications¶

Core Infrastructure¶

Spec	Component	Priority	Status
00	Implementation Overview	-	⏳ Planned
01	Architecture Design	-	⏳ Planned

Layer 1: Representation¶

Spec	Component	Priority	Status
02	Particle Memory	⭐ 1	⏳ Planned
03	Kernel Functions	⭐ 2	⏳ Planned

Layer 2: Reinforcement¶

Spec	Component	Priority	Status
04	MemoryUpdate Algorithm	⭐ 3	⏳ Planned
05	RF-SARSA Algorithm	⭐ 4	⏳ Planned

Layer 3: Inference¶

Spec	Component	Priority	Status
06	Policy Inference	⭐ 5	⏳ Planned
07	Soft State Transitions	⭐ 6	⏳ Planned

Layer 4: Abstraction (Part II)¶

Spec	Component	Priority	Status
08	Spectral Clustering	🔬 1	⏳ Planned
09	Concept Discovery	🔬 2	⏳ Planned
10	Concept Hierarchy	🔬 3	⏳ Planned
11	Concept-Conditioned Policies	🔬 4	⏳ Planned

Note: Part II implementation begins after Part I is validated (see Priority 7 below)

Demonstration Environment¶

Spec	Component	Priority	Status
12	2D Navigation Domain	⭐⭐ 7	⏳ Planned

Note: This is the primary environment for validating and demonstrating GRL-v0

Supporting Components¶

Spec	Component	Status
13	Environment Interface	⏳ Planned
14	Visualization Tools	⏳ Planned
15	Testing Strategy	⏳ Planned
16	Experiment Protocols	⏳ Planned

Implementation Priorities¶

Priority 1: Particle Memory ⭐¶

Why first: This IS the agent state. Everything else depends on it.

Key Features:

Particle storage: [(z_i, w_i)]
Energy queries: E(z) = -Σ w_i k(z, z_i)
Association: Find similar particles
Management: Add, merge, prune

Priority 2: Kernel Functions¶

Why second: Defines geometry of augmented space.

Key Features:

RBF kernel with ARD
Augmented kernel: k((s,θ), (s',θ'))
Gradient computation
Hyperparameter adaptation

Priority 3: MemoryUpdate (Algorithm 1)¶

Why third: The belief-state transition operator.

Key Features:

Particle instantiation
Kernel-based association
Weight propagation
Regularization

Priority 4: RF-SARSA (Algorithm 2)¶

Why fourth: Provides reinforcement signals.

Key Features:

Primitive SARSA layer
Field GP layer
Two-layer coupling
ARD updates

Priority 5: Policy Inference¶

Why fifth: How actions are selected.

Key Features:

Energy-based selection
Boltzmann sampling
Greedy mode
Gradient-based optimization (optional)

Priority 6: Soft State Transitions¶

Why sixth: Emergent uncertainty from kernel overlap.

Key Features:

Distributed successor states
Transition probability from kernel
Implicit POMDP interpretation
Uncertainty quantification

Why seventh: Primary validation and demonstration environment.

Purpose:

Reproduce the original paper (Figure 4, Section VI)
Validate all Part I components in a controlled setting
Demonstrate GRL capabilities professionally

Key Features:

Continuous 2D state space
Parametric movement actions (direction, magnitude)
Obstacles, walls, and goals
Energy landscape visualization
Particle memory visualization
Trajectory recording

Deployment Goals:

Reproducibility: Match original paper results
Professionalism: Publication-quality figures and demos
Accessibility:
Python API for programmatic use
Interactive web interface for exploration
Jupyter notebook tutorials
Extensibility: Easy to add new scenarios

See: 2D Navigation Specification below

Part II Priorities (After Part I Validated)¶

Priority 8: Spectral Clustering (Part II)¶

Why first in Part II: Foundation for concept discovery.

Key Features:

Kernel matrix construction from particle memory
Eigendecomposition
Cluster identification
Concept subspace projection (from quantum-inspired Chapter 05)

Priority 9: Concept Discovery¶

Why second: Automated structure learning.

Key Features:

Functional similarity metrics
Automatic concept identification
Concept naming/labeling
Validation metrics

Priority 10: Concept Hierarchy¶

Why third: Multi-level abstraction.

Key Features:

Nested subspace structure
Hierarchical composition
Transfer across concepts
Visualization

Priority 11: Concept-Conditioned Policies¶

Why fourth: Use discovered structure.

Key Features:

Policy per concept
Concept-gated execution
Hierarchical planning
Abstract reasoning

Overview¶

The 2D Navigation Domain is the primary environment for GRL-v0 validation and demonstration. Originally introduced in the paper (Figure 4, Section VI), we aim to:

Reproduce existing results with high fidelity
Enhance the domain to professional standards
Deploy as an accessible, interactive demonstration

Domain Description¶

State Space: $\mathcal{S} = [0, L_x] \times [0, L_y]$ (continuous 2D position)

Action Space: $\mathcal{A} = \{(\theta, v) : \theta \in [0, 2\pi), v \in [0, v_{\max}]\}$ - $\theta$: Direction angle - $v$: Speed magnitude

Augmented Space: $\mathcal{Z} = \mathcal{S} \times \mathcal{A}$ (4D continuous)

Dynamics: $$s_{t+1} = s_t + v \cdot (\cos\theta, \sin\theta) \cdot \Delta t$$

Obstacles: Polygonal or circular regions (configurable)

Goals: Target positions with rewards

Scenarios (From Original Paper)¶

Scenario 1: Simple goal-reaching - Single goal, no obstacles - Validate basic particle memory and policy inference

Scenario 2: Navigation with obstacles - Multiple obstacles (replicating Figure 4) - Demonstrate smooth navigation around barriers - Show energy landscape and particle distribution

Scenario 3: Multi-goal task - Multiple goals with different rewards - Demonstrate action-state duality - Show concept emergence (if Part II implemented)

Reproduction Goals¶

Figure 4 Recreation:

Exact environment setup from paper
Energy landscape visualization
Particle memory visualization
Learned trajectory comparison

Quantitative Metrics:

Success rate (reaching goal)
Path efficiency (vs. optimal)
Collision rate (with obstacles)
Learning curves (episodes to convergence)

Qualitative Assessment:

Smooth, natural trajectories
Efficient obstacle avoidance
Energy landscape interpretability

Professional Enhancement¶

Visual Quality:

Publication-ready figures (vector graphics)
Interactive animations (mp4/gif)
Real-time rendering (60 FPS)
Multiple view modes:
Top-down environment view
Energy landscape heatmap
Particle distribution overlay
Trajectory history

Code Quality:

Modular, extensible design
Configuration files (YAML/JSON)
Logging and metrics
Reproducible random seeds

Documentation:

API reference
Tutorial notebooks
Example scripts
Performance benchmarks

Deployment Plan¶

Phase 1: Core Implementation¶

Components:

Environment class (Nav2DEnv)
Rendering engine
Action space handling
Reward function

Deliverables:

Python package installable via pip
Basic visualization
Unit tests

Phase 2: GRL Integration¶

Components:

Particle memory integration
MemoryUpdate in navigation loop
RF-SARSA training
Energy landscape computation

Deliverables:

Training scripts
Evaluation scripts
Experiment configs

Phase 3: Professional Demo¶

Components:

Interactive Jupyter notebooks
Web-based interface (Flask/FastAPI + React)
Video demonstrations
Benchmark suite

Deliverables:

Hosted web demo (e.g., Hugging Face Spaces, Streamlit)
Tutorial video
Blog post

Web Interface Features¶

Interactive Controls:

Place obstacles (drag-and-drop)
Set goal positions
Adjust GRL hyperparameters (kernel bandwidth, temperature)
Start/stop/reset simulation

Visualizations:

Real-time agent movement
Energy landscape evolution
Particle memory growth
Learning curves

Export:

Save trajectories
Download figures
Export particle memory

Sharing:

Permalink to configurations
Embed in documentation
Public gallery of scenarios

API Design¶

from grl.envs import Nav2DEnv
from grl.agents import GRLAgent

# Create environment
env = Nav2DEnv(
    size=(10, 10),
    obstacles=[
        {"type": "circle", "center": (5, 5), "radius": 1.5},
        {"type": "polygon", "vertices": [(2, 2), (3, 2), (3, 3)]},
    ],
    goal=(9, 9),
    goal_reward=10.0,
)

# Create GRL agent
agent = GRLAgent(
    kernel="rbf",
    lengthscale=1.0,
    temperature=0.1,
)

# Training loop
for episode in range(num_episodes):
    state = env.reset()
    done = False

    while not done:
        action = agent.act(state)
        next_state, reward, done, info = env.step(action)
        agent.update(state, action, reward, next_state)
        state = next_state

    # Visualize
    if episode % 10 == 0:
        env.render(show_particles=True, show_energy=True)
        agent.save_memory(f"memory_ep{episode}.pkl")

# Evaluate
success_rate, avg_path_length = evaluate(agent, env, num_trials=100)

Timeline¶

Week 1-2: Core environment implementation Week 3-4: GRL integration and training Week 5-6: Visualization and demos Week 7-8: Web interface and deployment Week 9-10: Documentation and tutorials

Target: Complete professional 2D navigation demo by March 2026

Philosophy: GRL as a Generalization¶

Key Insight: Traditional RL is a special case of GRL when:

Action space is discrete → GRL with fixed parametric mappings
Action space is finite → GRL with finite operator set
Q-learning → GRL with trivial augmentation (state only)

Strategic Goal: Demonstrate that GRL subsumes classical RL, including modern applications like:

RLHF for LLMs (Reinforcement Learning from Human Feedback)
PPO/SAC for continuous control
DQN for discrete actions
Actor-critic methods

This positions GRL not as "another RL algorithm" but as a unifying framework that recovers existing methods as special cases while enabling new capabilities.

Priority Application Domains¶

Tier 1: Validation Environments (Demonstrate Correctness)¶

Goal: Show GRL recovers classical RL results

Domain	Type	Classical Baseline	GRL Advantage	Status
2D Navigation	Continuous control	N/A (novel)	Smooth generalization	⏳ Priority 7
CartPole	Discrete control	DQN	Continuous action variant	📋 Planned
Pendulum	Continuous control	DDPG, SAC	Parametric torque	📋 Planned
MuJoCo Ant	Robotics	PPO, SAC	Compositional gaits	📋 Planned

Tier 2: Strategic Environments (Demonstrate Generality)¶

Goal: Show GRL applies to modern RL problems, including LLMs

Note: These are theoretical connections with potential future implementations. Each would require significant engineering effort.

Domain	Type	Why Important	Theoretical Connection	Implementation
LLM Fine-tuning (RLHF)	Discrete (tokens)	Massive industry relevance	Token selection as discrete action, PPO as special case	🔬 Exploratory
Prompt Optimization	Discrete sequences	Growing field	Parametric prompt generation in embedding space	🔬 Exploratory
Molecule Design	Graph generation	Drug discovery	Parametric molecule operators	🔬 Exploratory
Neural Architecture Search	Discrete choices	AutoML	Compositional architecture operators	🔬 Exploratory

Primary Value: Demonstrating that GRL theoretically generalizes existing methods used in commercially relevant problems (RLHF, prompt tuning, etc.)

Implementation Reality: These are massive undertakings comparable to full research projects. They serve as:

Motivation for why GRL matters
Future directions if resources/collaborators available
Examples in theoretical justification documents

Tier 3: Novel Environments (Demonstrate Unique Capabilities)¶

Goal: Show what GRL enables that classical RL cannot do easily

Domain	Type	Novel Capability	Why GRL Shines	Status
Physics Simulation	Continuous fields	Apply force fields, not point forces	Operator actions on state space	📋 Planned
Fluid Control	PDE-governed	Manipulate flow fields	Field operators, neural operators	📋 Planned
Image Editing	High-dim continuous	Parametric transformations	Smooth action manifolds	📋 Planned
Multi-Robot Coordination	Continuous, multi-agent	Compositional team behaviors	Operator algebra	📋 Planned

Recovering Classical RL: A Bridge to Adoption¶

Document: docs/GRL0/recovering_classical_rl.md (to be created)

Purpose: Show step-by-step how classical RL algorithms emerge from GRL as special cases

Contents:

Q-learning from GRL
Discrete action space as fixed parametric mapping
Particle memory as replay buffer
TD update as special case of MemoryUpdate
DQN from GRL
Neural network Q-function as continuous approximation of particle field
Experience replay as particle subsampling
Target networks as delayed MemoryUpdate
Policy Gradient (REINFORCE) from GRL
Boltzmann policy from energy landscape
Score function gradient as field gradient
Baseline as energy normalization
Actor-Critic (PPO, SAC) from GRL
Actor = policy inference from field
Critic = reinforcement field itself
Entropy regularization as temperature parameter
RLHF for LLMs from GRL
Token selection as discrete action
Reward model as energy function
PPO update as special case of RF-SARSA

Impact: This document becomes the key reference for convincing classical RL researchers that GRL is not alien, but a natural generalization.

LLM Fine-tuning as a GRL Application (Exploratory)¶

Status: Theoretical connection established, implementation exploratory

Why This Is Interesting:

Relevance: RLHF is used for ChatGPT, Claude, Llama, Gemini
Familiarity: Most ML researchers understand this problem
Validation: If GRL generalizes RLHF theoretically, it validates the framework's breadth

Theoretical Formulation¶

State: $s_t$ = (prompt, partial response up to token $t$)

Action: $a_t \in \mathcal{V}$ where $\mathcal{V}$ = vocabulary (discrete)

GRL View:

Augmented space: $(s_t, \theta_t)$ where $\theta_t$ represents token choice
Particle memory: stores (prompt, response, reward) experiences
Reinforcement field: $Q^+(s_t, \theta_t)$ over semantic embedding
Policy inference: Sample from Boltzmann over $Q^+$

Key Insight: Standard RLHF (PPO) is GRL with:

Discrete action space (tokens)
Neural network approximation of field
On-policy sampling

This theoretical connection is documented in: Recovering Classical RL from GRL

Potential GRL Advantages (Theoretical)¶

Off-policy learning: Particle memory could enable experience replay
Smooth generalization: Nearby prompts might share value via kernel
Uncertainty: Sparse particles could indicate high uncertainty
Interpretability: Energy landscape over prompt space

However: These advantages are speculative without empirical validation.

Implementation Reality¶

Challenges:

Infrastructure: Requires reward model training, human feedback data, preference datasets
Computational cost: LLM fine-tuning is expensive (even GPT-2)
Comparison difficulty: Matching PPO requires careful hyperparameter tuning
Integration: Modern RLHF uses TRL, transformers, accelerate — non-trivial to integrate
Validation: Showing clear advantages requires extensive controlled experiments

Estimated Effort: 6-12 months of focused work with GPU resources

When to Pursue:

✅ After GRL validated on simpler environments (2D Nav, classical RL)
✅ If collaborators or funding available
✅ If clear path to demonstrating advantages
✅ If access to human feedback datasets

Realistic First Step (if pursued):

Toy RLHF-like problem (small vocabulary, simple preference task)
Not real LLM, but demonstrates GRL can handle discrete sequential choices
Fast iteration, low compute cost

Environment Simulation Package Structure¶

Given the scope of applications, we'll need a well-organized environment package:

src/grl/envs/
├── __init__.py
├── base_env.py                 # GRL environment interface
│
├── validation/                 # Tier 1: Classical RL baselines
│   ├── nav2d.py               # 2D navigation (Priority 7)
│   ├── cartpole.py            # Discrete control
│   ├── pendulum.py            # Continuous control
│   └── mujoco_envs.py         # Robotics (Ant, Humanoid)
│
├── strategic/                  # Tier 2: Modern RL applications
│   ├── llm_finetuning.py      # 🔥 RLHF for LLMs (High Priority)
│   ├── prompt_optimization.py  # Prompt tuning
│   ├── molecule_design.py      # Drug discovery
│   └── nas.py                  # Neural Architecture Search
│
├── novel/                      # Tier 3: GRL-native applications
│   ├── physics_sim.py          # Force field control
│   ├── fluid_control.py        # PDE-governed systems
│   ├── image_editing.py        # Parametric image transforms
│   └── multi_robot.py          # Multi-agent coordination
│
├── wrappers/                   # Adapters for existing environments
│   ├── gym_wrapper.py          # OpenAI Gym → GRL
│   ├── gymnasium_wrapper.py    # Gymnasium → GRL
│   ├── dm_control_wrapper.py   # DeepMind Control → GRL
│   └── rlhf_wrapper.py         # TRL/transformers → GRL
│
└── scenarios/                  # Predefined configurations
    ├── paper_scenarios.py      # Scenarios from original paper
    ├── benchmark_suite.py      # Standard benchmarks
    └── tutorials.py            # Teaching examples

Key Design Principle:

Wrappers allow GRL to be applied to any existing RL environment
Native environments showcase GRL's unique capabilities
Scenarios provide reproducible experiments

Strategic Roadmap Update¶

Phase 1 (Q1 2026): Foundation ⭐⭐⭐ - Complete Part I tutorial - Implement core GRL components - ✅ 2D Navigation validated

Phase 2 (Q2 2026): Classical RL Recovery ⭐⭐ - Implement wrappers (Gym, Gymnasium) - Reproduce DQN on CartPole - Reproduce SAC on Pendulum - Document: "Recovering Classical RL from GRL" ✅ Complete - Paper A submission

Phase 3 (Q3-Q4 2026): Novel Contributions ⭐ - Amplitude-based RL (if promising) - MDL consolidation - Concept-based mixture of experts - Papers B & C submissions

Future Directions (No timeline):

Theoretical articles: Justify how RLHF, prompt optimization, molecule design are special cases
Implementation: If resources/collaborators available, pick 1-2 strategic applications
Novel applications: Physics simulation, multi-robot coordination (GRL-native capabilities)

Success Metrics¶

Technical (Achievable):

2D Navigation demo complete with professional web interface
GRL recovers DQN/SAC results on classical benchmarks (±5% performance)
Classical RL wrappers work with existing environments
Documentation complete and accessible

Research (Achievable):

Part I tutorial complete (Chapters 0-10)
Part II foundation (concept subspaces formalized)
"Recovering Classical RL" document demonstrates generality
Paper A submitted (operator formalism)
1-2 papers on novel contributions (amplitude-based RL or MDL consolidation)

Adoption (Aspirational):

GitHub stars: 100+ (realistic), 1000+ (stretch)
External users beyond our lab
Cited in other papers
Conference workshop or tutorial (if invited)

Strategic Applications (Aspirational, No Timeline):

Theoretical articles justify RLHF/prompt-opt as special cases
If resources available: implement 1-2 strategic applications
Industry partnerships (if opportunities arise)

Code Structure¶

src/grl/
├── __init__.py
├── core/
│   ├── particle_memory.py          # Priority 1: Particle state
│   ├── kernels.py                  # Priority 2: RKHS geometry
│   └── soft_transitions.py         # Priority 6: Emergent uncertainty
├── algorithms/
│   ├── memory_update.py            # Priority 3: Belief transition
│   ├── rf_sarsa.py                 # Priority 4: TD learning
│   └── policy_inference.py         # Priority 5: Action selection
├── concepts/                        # Part II: Emergent Structure
│   ├── spectral_clustering.py      # Priority 8: Functional clustering
│   ├── concept_discovery.py        # Priority 9: Automated structure
│   ├── concept_hierarchy.py        # Priority 10: Multi-level abstraction
│   └── concept_policies.py         # Priority 11: Hierarchical control
├── envs/
│   ├── nav2d.py                    # Priority 7: 2D Navigation Domain
│   ├── scenarios.py                # Predefined scenarios (Figure 4)
│   └── base_env.py                 # Environment interface
├── agents/
│   ├── grl_agent.py                # Complete GRL agent
│   └── evaluation.py               # Agent evaluation tools
├── utils/
│   ├── config.py                   # Configuration management
│   ├── reproducibility.py          # Random seeds, determinism
│   └── metrics.py                  # Performance metrics
├── visualization/
│   ├── energy_landscape.py         # Energy field heatmaps
│   ├── particle_viz.py             # Particle memory plots
│   ├── trajectory_viz.py           # Agent trajectories
│   └── concept_viz.py              # Concept subspace plots (Part II)
└── web/                            # Web deployment (Priority 7)
    ├── api.py                      # FastAPI backend
    ├── static/                     # Frontend assets
    └── templates/                  # HTML templates

Dependencies¶

Core Dependencies¶

torch >= 2.0              # Neural operators, gradient computation
numpy >= 1.24             # Numerical operations
scipy >= 1.10             # Scientific computing, optimization
gpytorch >= 1.10          # Gaussian processes (optional)
scikit-learn >= 1.3       # Spectral clustering (Part II)

Visualization¶

matplotlib >= 3.7         # Static plots
seaborn >= 0.12          # Statistical visualization
plotly >= 5.14           # Interactive plots

Web Deployment (Priority 7)¶

fastapi >= 0.104         # Backend API
uvicorn >= 0.24          # ASGI server
pydantic >= 2.4          # Data validation
jinja2 >= 3.1            # Templating

Development¶

pytest >= 7.4            # Testing
black >= 23.9            # Code formatting
mypy >= 1.6              # Type checking
sphinx >= 7.2            # Documentation

Quality Standards¶

Code Quality¶

All public functions have docstrings (NumPy style)
Type hints throughout (Python 3.10+)
Unit test coverage > 80%
No linting errors (black, mypy, flake8)
Examples run without modification
Math notation matches paper

Part I Validation¶

Reproduce original paper results (Figure 4)
MemoryUpdate converges
RF-SARSA learns effectively
Energy landscapes are smooth
Particle memory grows/prunes correctly

Part II Validation (After Part I)¶

Spectral clustering identifies meaningful concepts
Concept hierarchy is interpretable
Concept-conditioned policies improve performance
Transfer learning across concepts works

Web interface is responsive and intuitive
Visualizations render at 60 FPS
All scenarios from paper work
Export/sharing functionality works
Tutorial notebooks are clear and complete

Summary¶

GRL-v0 Implementation spans:

Part I (Layers 1-3): Particle-based reinforcement fields
Part II (Layer 4): Emergent structure and concept discovery
Demonstration: Professional 2D navigation domain with web deployment

Priority Order:

Part I foundations (Priorities 1-6)
2D Navigation validation (Priority 7) ⭐⭐ Critical milestone
Part II extensions (Priorities 8-11)
Additional environments and experiments

Target: Complete 2D navigation demo by March 2026

See also: Research Roadmap for broader research plan

Last Updated: January 14, 2026

GRL-v0 Implementation Guide¶

Overview¶

Architecture Overview¶

Implementation Specifications¶

Core Infrastructure¶

Layer 1: Representation¶

Layer 2: Reinforcement¶

Layer 3: Inference¶

Layer 4: Abstraction (Part II)¶

Demonstration Environment¶

Supporting Components¶

Implementation Priorities¶

Priority 1: Particle Memory ⭐¶

Priority 2: Kernel Functions¶

Priority 3: MemoryUpdate (Algorithm 1)¶

Priority 4: RF-SARSA (Algorithm 2)¶

Priority 5: Policy Inference¶

Priority 6: Soft State Transitions¶

Priority 7: 2D Navigation Domain ⭐⭐ Critical¶

Part II Priorities (After Part I Validated)¶

Priority 8: Spectral Clustering (Part II)¶

Priority 9: Concept Discovery¶

Priority 10: Concept Hierarchy¶

Priority 11: Concept-Conditioned Policies¶

2D Navigation Domain Specification¶

Overview¶

Domain Description¶

Scenarios (From Original Paper)¶

Reproduction Goals¶

Professional Enhancement¶

Deployment Plan¶

Phase 1: Core Implementation¶

Phase 2: GRL Integration¶

Phase 3: Professional Demo¶

Web Interface Features¶

API Design¶

Timeline¶

Application Domains Beyond 2D Navigation¶

Philosophy: GRL as a Generalization¶

Priority Application Domains¶

Tier 1: Validation Environments (Demonstrate Correctness)¶

Tier 2: Strategic Environments (Demonstrate Generality)¶

Tier 3: Novel Environments (Demonstrate Unique Capabilities)¶

Recovering Classical RL: A Bridge to Adoption¶

LLM Fine-tuning as a GRL Application (Exploratory)¶

Theoretical Formulation¶

Potential GRL Advantages (Theoretical)¶

Implementation Reality¶

Environment Simulation Package Structure¶

Strategic Roadmap Update¶

Success Metrics¶

Code Structure¶

Dependencies¶

Core Dependencies¶

Visualization¶

Web Deployment (Priority 7)¶

Development¶

Quality Standards¶

Code Quality¶

Part I Validation¶

Part II Validation (After Part I)¶

2D Navigation Demo¶

Summary¶