# genpark-q-learning-temporal-difference-agent-skill

> genpark-q-learning-temporal-difference-agent-skill — alphaparkinc-genpark-q-learning-temporal-difference-agent-skill. Use this tool when you need to implement a tabular Q-learning algorithm for decision-making and optimization in complex environments. It solves problems related to sequential decision-making and policy learning, using epsilon-greedy exploration and Bellman state-action updates to improve agent performance. The agent takes in state and action data as inputs and outputs optimal policies, making it suitable for use cases involving reinforcement learning and autonomous agents.

Canonical page: https://skillsregistry.net/skills/alphaparkinc-genpark-q-learning-temporal-difference-agent-skill  
JSON: https://api.skillsregistry.net/v1/skills/alphaparkinc-genpark-q-learning-temporal-difference-agent-skill

## Description

Tabular Q-learning temporal difference agent with epsilon-greedy exploration and Bellman state-action updates.

## Trust

- **Trust score (0–1):** 0.50
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/alphaparkinc/genpark-q-learning-temporal-difference-agent-skill)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "alphaparkinc-genpark-q-learning-temporal-difference-agent-skill"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/alphaparkinc-genpark-q-learning-temporal-difference-agent-skill` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/alphaparkinc-genpark-q-learning-temporal-difference-agent-skill/pull`

---
SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt