Reinforcement learning · MLD-0898
Policy
A rule or model mapping available information to actions or action probabilities.
Original reference page · detailed expansion pending. Original definitions, examples, notes and citations are preserved below. New examples are illustrative, not benchmark results.
Original example
Choose the next move from an observed state.
Original caveat
A policy can be deterministic or stochastic.
Context and aliases
Level: Intermediate. Stages: Theory · Training · Evaluation.
Tags: RL · DL · SLM · LLM.
Aliases: None recorded.
Related concepts
Concepts linking here
Technology connections
- Brax — JAX-based reinforcement-learning training utilities with legacy physics/environment components.
- CleanRL — Single-file implementations of deep RL algorithms.
- Gymnasium — Standard API and environment ecosystem for reinforcement learning.
- MuJoCo — Physics engine for articulated-body simulation.
- NVIDIA Isaac Sim — Robotics simulation platform with rendering and sensor tools.
- PettingZoo — API and environment collection for multi-agent reinforcement learning.
- RLlib — Distributed reinforcement-learning library in Ray.
- Stable-Baselines3 — Tested implementations of common RL algorithms in PyTorch.
- TorchRL — PyTorch components for reinforcement learning.
Original references
- OpenAI Spinning Up: reinforcement learning ↗ · Consulted
- TRL documentation ↗ · Consulted
- Original reference URL ↗