Reinforcement Learning Designer

Guides the design of reinforcement learning systems including policy selection (PPO, SAC, DQN), environment design, reward shaping, exploration strategies, multi-agent configurations, and sim-to-real transfer for robotics and game AI.

Gold

v1.0.00 activationsAI & Machine LearningTechnologyexpert

SupaScore

84.15

Research Quality (15%)

8.6

Prompt Engineering (25%)

8.5

Practical Utility (15%)

8.3

Completeness (10%)

8.4

User Satisfaction (20%)

8.2

Decision Usefulness (15%)

8.5

Best for

▸Design reward functions for autonomous vehicle training with safety constraints
▸Select RL algorithms for continuous control robotics applications like robotic arm manipulation
▸Architect multi-agent RL systems for trading bots or resource allocation
▸Implement sim-to-real transfer pipelines for robotic policy deployment
▸Design exploration strategies for sparse reward environments like game AI

What you'll get

●Detailed MDP formalization with state/action space definitions, algorithm comparison table with sample efficiency metrics, and reward function pseudocode with safety constraints
●Multi-agent system architecture diagram showing communication patterns, individual agent policies, and centralized training approach with implementation timeline
●Sim-to-real transfer pipeline with domain randomization parameters, reality gap analysis, and progressive deployment strategy with success metrics

Not designed for ↓

×Traditional supervised learning tasks with labeled datasets
×Natural language processing or computer vision model architectures
×Statistical analysis or descriptive analytics on historical data
×Basic machine learning model evaluation metrics

Expects

Clear problem description including environment characteristics, action/state spaces, reward structure, and deployment constraints (sim vs real-world).

Returns

Structured RL system design with algorithm selection justification, reward function specification, exploration strategy, and implementation roadmap with specific hyperparameters.

Evidence Policy

Enabled: this skill cites sources and distinguishes evidence from opinion.

reinforcement-learningpposacdqnreward-shapingmulti-agentsim-to-realpolicy-gradientexplorationmdproboticsgame-ai

Research Foundation: 8 sources (1 books, 6 paper, 1 official docs)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

Version History

v1.0.02/15/2026

Initial release

Prerequisites

Use these skills first for best results.

Supervised Learning EngineerGold

Works well with

Drift Monitoring Pipeline DesignerGold Hyperparameter Tuning ExpertGold ML Experiment TrackerGold Model Deployment OptimizerPlatinum PyTorch Deep Learning EngineerPlatinum

Need more depth?

Specialist skills that go deeper in areas this skill touches.

PyTorch Lightning EngineerGold Distributed Tracing EngineerGold Kubernetes Operations AdvisorGold

Common Workflows

RL Production Pipeline

End-to-end RL system from design through production deployment with monitoring

reinforcement-learning-designer→PyTorch Lightning Engineer→ML Experiment Tracker→Model Deployment Optimizer

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