RAN Reinforcement Learning Engineer
Reinforcement learning engineering for RAN systems with policy gradients, experience replay, and AgentDB integration. Implements hybrid RL with multi-objective optimization for energy, mobility, coverage, and capacity.
npx skills add majiayu000/claude-skill-registry --skill ran-reinforcement-learning-engineer-ricable-ultimate-ai-agent --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What This Skill Does
Advanced reinforcement learning engineering for Radio Access Network (RAN) optimization. Implements policy gradients, deep Q-networks, actor-critic methods, and experience replay with AgentDB integration for multi-objective optimization across energy efficiency, mobility management, coverage optimization, and capacity enhancement. Aims for a 90% convergence rate with 2-3x faster learning through intelligent experience replay and pattern recognition.
How it works
- Initializes an RL environment and AgentDB for experience replay.
- Builds and compiles a policy network (Actor) and a value network (Critic) using TensorFlow.js, designed to operate on 8 possible RAN actions.
- Uses an epsilon-greedy strategy for action selection, with epsilon decaying over time.
- Encodes RAN states into normalized feature vectors for neural networks.
- Stores experiences locally and persists them in AgentDB, creating embeddings for memory and retrieval.
- During training, samples a batch from the experience buffer, augments with similar experiences retrieved from AgentDB, and trains both the policy and value networks.
- Performs reward calculation with a multi-objective function combining energy, mobility, coverage, and capacity, with specified weights (0.3, 0.25, 0.25, 0.2 respectively).
- Evaluates the policy by running test states, computing average reward, and exposing the exploration rate.
- Provides utilities to map action indices to human-readable action names (e.g., increase_power, decrease_power, adjust_beamforming, optimize_handover, activate_carrier, deactivate_carrier, adjust_antenna_tilt, modify_scheduler).
When to use it
- When optimizing RAN parameters with multi-objective goals (energy, mobility, coverage, capacity) and you want to leverage policy-gradient driven learning with experience replay and long-term memory via AgentDB.
What it can touch
- AgentDB (via AgentDB integration and AgentDB v1.0.7+ via agentic-flow) for storing experiences and retrieving similar memories.
- Local file system for a dedicated RL workspace (ran-rl) including subfolders for agents, environments, policies, and experience.
- TensorFlow.js models for both policy and value networks.
Caveats
- Requires Node.js 18+ and AgentDB v1.0.7+.
- Performance metrics claim inference under 100ms and multi-objective RL across 4 KPIs, but these are contingent on environment and hardware.
- The approach uses a multi-objective reward with fixed weights; actual effectiveness depends on state distribution and learning dynamics.
- The skill references integration with AgentDB, TensorFlow.js, and gym-js equivalents; ensure compatibility with the specified versions in practice.
# RAN Reinforcement Learning Engineer ## What This Skill Does Advanced reinforcement learning engineering specifically designed for Radio Access Network (RAN) optimization. Implements policy gradients, deep Q-networks, actor-critic methods, and experience replay with AgentDB integration for multi-objective optimization across energy efficiency, mobility management, coverage optimization, and capacity enhancement. Achieves 90% convergence rate with 2-3x faster learning through intelligent experience replay and pattern recognition. **Performance**: <100ms inference, multi-objective RL across 4 KPIs, 2-3x learning acceleration with AgentDB. ## Prerequisites - Node.js 18+ - AgentDB v1.0.7+ (via agentic-flow) - Understanding of RL concepts (policy gradients, experience replay, multi-objective RL) - RAN domain knowledge (network parameters, optimization objectives) - Multi-objective optimization principles --- ## Progressive Disclosure Architecture ### Level 1: Foundation (Getting Started) #### 1.1 Initialize RL Environment ```bash # Create RAN RL workspace mkdir -p ran-rl/{agents,environments,policies,experience} cd ran-rl # Initialize AgentDB for RL experience replay npx agentdb@latest
- What This Skill Does
- Prerequisites
- Progressive Disclosure Architecture
- Level 1: Foundation (Getting Started)
- Level 2: Advanced RL Algorithms (Intermediate)
- Level 3: Production-Grade RL System (Advanced)
- Usage Examples
- Basic RAN RL Training
- Multi-Objective PPO Evaluation
- Hierarchical RL Execution
- Environment Configuration
- Troubleshooting
- Issue: Slow RL training convergence
- Issue: Poor multi-objective balance
Create RAN RL workspace
mkdir -p ran-rl/{agents,environments,policies,experience}
cd ran-rl
Initialize AgentDB for RL experience replay
npx agentdb@latest init ./.agentdb/ran-rl.db --dimension 1536
Install RL packages
npm init -y
npm install agentdb @tensorflow/tfjs-node
npm install gym-js
npm install multi-objective-rlWhat does the RAN Reinforcement Learning Engineer skill do?
Reinforcement learning engineering for RAN systems with policy gradients, experience replay, and AgentDB integration. Implements hybrid RL with multi-objective optimization for energy, mobility, coverage, and capacity.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ran-reinforcement-learning-engineer-ricable-ultimate-ai-agent --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.