Reinforcement learning · MLD-0371
Environment
The system an agent interacts with, receiving observations and producing consequences and rewards.
Original reference page · detailed expansion pending. Original definitions, examples, notes and citations are preserved below. New examples are illustrative, not benchmark results.
Original example
A game, simulator, or tool-enabled task setting.
Original caveat
The environment can include other agents and hidden variables.
Context and aliases
Level: Intermediate. Stages: Theory · Training · Evaluation.
Tags: RL · DL · SLM · LLM.
Aliases: None recorded.
Related concepts
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 ↗