State of Implementation Stack in Reinforcement Learning

calendar Nov 8, 2025 · 3 min read · rl  ·
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Classical Reinforcement Learning

Frameworks that emphasize traditional RL algorithms and standardized environments for benchmarking.

Environment Libraries

  1. Gymnasium (formerly OpenAI Gym) Standardized toolkit providing a wide range of benchmark environments for training and evaluating RL agents.

  2. PettingZoo (for Multi-Agent RL) Companion to Gymnasium, offering structured environments for multi-agent reinforcement learning tasks.

  3. PyBullet / MuJoCo / Isaac Gym (for Physics-Based Simulation) High-fidelity physics simulators supporting robotic and control-oriented RL experiments.

  4. Minigrid and MiniWorld (Lightweight, Fast Simulation Environments) Minimalistic grid-based and 3D simulation environments optimized for fast prototyping and testing.

Algorithm Libraries

  • Stable Baselines & Stable Baselines3 Widely used implementations of classical RL algorithms like PPO, DQN, and A2C with clean APIs and reproducible results.

  • RL-Glue Early framework providing a standard interface for RL agents and environments to facilitate consistent experimentation.

  • PyRL / MushroomRL Modular libraries emphasizing clarity, reproducibility, and support for classical RL methods and policy evaluation.

Deep Reinforcement Learning

Frameworks designed for modern, large-scale, and neural-network-based RL.

PyTorch-Based Frameworks

  • TorchRL (by Meta AI) Native PyTorch library focusing on flexibility, composability, and distributed training capabilities.

  • CleanRL Lightweight, single-file RL implementations promoting simplicity, transparency, and reproducibility.

  • RL Zoo / SB3-Contrib Community-driven extensions for Stable Baselines3, offering tuned hyperparameters and experimental algorithms.

TensorFlow-Based Frameworks

  • TF-Agents (by Google) TensorFlow-native RL framework providing modular components, tight integration with TFX, and TensorBoard support.

  • Keras-RL / Keras-RL2 Easy-to-use RL interface for Keras models, suitable for educational and prototyping purposes.

Scalable and Distributed RL Frameworks

  • Ray RLlib Scalable library built on Ray for distributed training; supports multi-agent, offline, and hierarchical RL.

  • Acme (by DeepMind) Research-oriented, modular framework implementing advanced RL agents like IMPALA and R2D2.

  • Tianshou Lightweight PyTorch-based library emphasizing flexibility, efficiency, and offline RL capabilities.

  • Coach (Intel AI Lab) RL research framework emphasizing reproducibility, benchmarking, and extensive algorithm coverage.

Specialized and Emerging Frameworks

Multi-Agent Reinforcement Learning (MARL)

  • PettingZoo Standardized set of multi-agent environments compatible with major RL frameworks.

  • Mava (by DeepMind / InstaDeep) Framework for scalable and composable multi-agent RL built on top of Acme.

  • PyMARL / PyMARL2 Research-focused MARL platforms supporting popular algorithms like QMIX and VDN.

Offline and Imitation Learning Frameworks

  • D3RLpy Comprehensive library for offline RL and imitation learning with strong benchmarking support.

  • RLPyt High-performance PyTorch framework optimized for parallel sampling and large-scale RL experiments.

  • Imitation (from the Stable Baselines ecosystem) Toolkit for behavior cloning, inverse RL, and imitation learning built atop Stable Baselines3.

Simulation and Integration Frameworks

  • Unity ML-Agents Unity-based platform enabling interactive 3D simulations and environment design for RL research.

  • CARLA Open-source simulator for autonomous driving research and policy training in realistic environments.

  • AirSim Microsoft’s simulator for aerial and autonomous vehicles, supporting photorealistic and physics-accurate training.