How do I install the Rl Training skill?

Run npx skillmds@latest add tomevault-io/rl-training in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Rl Training safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Rl Training?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Rl Training free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Rl Training?

tomevault-io (@tomevault-io) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Rl Training by tomevault-io — Agent Skill

Name: Rl Training
Author: tomevault-io

File contents

Reinforcement Learning Training Skill

You are an expert in reinforcement learning, specifically Deep Q-Networks (DQN) for game AI.

Project Context

This CTF-AI project uses:

DQN Agent with Double DQN, Prioritized Experience Replay, Huber loss
19-dimensional state vector: player(5) + target(6) + enemy(4) + global(4)
3 actions: DEFENCE(0), SCORING(1), SAVING(2)
Gymnasium environment for training interface
PyTorch for neural network

Key Files

backend/lib/reinforcement_learning/agent.py - DQNAgent class
backend/lib/reinforcement_learning/network.py - Neural network architecture
backend/lib/reinforcement_learning/reward_calculator.py - Reward function
backend/lib/reinforcement_learning/state_extractor.py - Feature extraction
backend/lib/reinforcement_learning/training/train_gym.py - Training script

When Asked to Train

# Offline training
python3 -m lib.reinforcement_learning.training.train_gym 8080 --algorithm CustomDQN --train-offline

# Online training (requires game server)
python3 -m lib.reinforcement_learning.training.train_gym 34712 --algorithm CustomDQN

Hyperparameter Guidelines

Parameter	Default	Tuning Advice
Learning rate	0.0005	Lower (0.0001) if unstable, higher (0.001) if slow
Epsilon decay	0.998	Slower (0.999) for more exploration
Gamma	0.99	Lower (0.95) for shorter-term focus
Batch size	32	Larger (64, 128) for more stable gradients
Target update	50	More frequent (20) for faster learning

Debugging Training Issues

Reward not improving: Check reward_calculator.py, ensure rewards are balanced
Q-values exploding: Enable gradient clipping, reduce learning rate
Agent stuck in one action: Increase exploration (epsilon), check state features
Training unstable: Enable Double DQN, use Huber loss, increase buffer size

Reward Tuning

Current reward structure:

Score flag: +150 (primary objective)
Pick up flag: +10
Lose flag: -40
Get captured: -25
Step penalty: -0.02

Adjust in reward_calculator.py if agent behavior is suboptimal.

Source: sdd330/ctf-ai — distributed by TomeVault.

---name: rl-trainingdescription: Reinforcement learning training for CTF-AI. Use when training DQN agents, adjusting hyperparameters, debugging training issues, analyzing rewards, or improving AI performance. Use when this capability is needed.---# Reinforcement Learning Training SkillYou are an expert in reinforcement learning, specifically Deep Q-Networks (DQN) for game AI.## Project ContextThis CTF-AI project uses:- **DQN Agent** with Double DQN, Prioritized Experience Replay, Huber loss- **19-dimensional state vector**: player(5) + target(6) + enemy(4) + global(4)- **3 actions**: DEFENCE(0), SCORING(1), SAVING(2)- **Gymnasium environment** for training interface- **PyTorch** for neural network## Key Files- `backend/lib/reinforcement_learning/agent.py` - DQNAgent class- `backend/lib/reinforcement_learning/network.py` - Neural network architecture- `backend/lib/reinforcement_learning/reward_calculator.py` - Reward function- `backend/lib/reinforcement_learning/state_extractor.py` - Feature extraction- `backend/lib/reinforcement_learning/training/train_gym.py` - Training script## When Asked to Train```bash# Offline trainingpython3 -m lib.reinforcement_learning.training.train_gym 8080 --algorithm CustomDQN --train-offline# Online training (requires game server)python3 -m lib.reinforcement_learning.training.train_gym 34712 --algorithm CustomDQN```## Hyperparameter Guidelines| Parameter | Default | Tuning Advice ||-----------|---------|---------------|| Learning rate | 0.0005 | Lower (0.0001) if unstable, higher (0.001) if slow || Epsilon decay | 0.998 | Slower (0.999) for more exploration || Gamma | 0.99 | Lower (0.95) for shorter-term focus || Batch size | 32 | Larger (64, 128) for more stable gradients || Target update | 50 | More frequent (20) for faster learning |## Debugging Training Issues1. **Reward not improving**: Check reward_calculator.py, ensure rewards are balanced2. **Q-values exploding**: Enable gradient clipping, reduce learning rate3. **Agent stuck in one action**: Increase exploration (epsilon), check state features4. **Training unstable**: Enable Double DQN, use Huber loss, increase buffer size## Reward TuningCurrent reward structure:- Score flag: +150 (primary objective)- Pick up flag: +10- Lose flag: -40- Get captured: -25- Step penalty: -0.02Adjust in `reward_calculator.py` if agent behavior is suboptimal.---> Source: [sdd330/ctf-ai](https://github.com/sdd330/ctf-ai) — distributed by [TomeVault](https://tomevault.io).

Frequently asked questions

Rl Training