SLM Lab
v4.2.3
v4.2.3
  • SLM Lab
  • 🖥Setup
    • Installation
    • Quick Start
  • 🚀Using SLM Lab
    • Lab Command
    • Lab Organization
    • Train: REINFORCE CartPole
    • Resume and Enjoy: REINFORCE CartPole
    • Agent Spec: DDQN+PER on LunarLander
    • Env Spec: A2C on Pong
    • GPU Usage: PPO on Pong
    • Parallelizing Training: Async SAC on Humanoid
    • Experiment and Search Spec: PPO on Breakout
    • Run Benchmark: A2C on Atari Games
    • Meta Spec: High Level Specifications
    • Post-Hoc Analysis
    • TensorBoard: Visualizing Models and Actions
    • Using SLM Lab In Your Project
  • 📈Analyzing Results
    • Data Locations
    • Graphs and Data
    • Performance Metrics
  • 🥇Benchmark Results
    • Public Benchmark Data
    • Discrete Environment Benchmark
    • Continuous Environment Benchmark
    • Atari Environment Benchmark
    • RL GIFs
  • 🔧Development
    • Modular Design
      • Algorithm Taxonomy
      • Class Inheritance: A2C > PPO
    • Algorithm
      • DQN
      • REINFORCE
      • Actor Critic
    • Memory
      • Replay
      • PrioritizedReplay
      • OnPolicyReplay
      • OnPolicyBatchReplay
    • Net
      • MLP
      • CNN
      • RNN
    • Profiling SLM Lab
  • 📖Publications and Talks
    • Book: Foundations of Deep Reinforcement Learning
    • Talks and Presentations
  • 🤓Resources
    • Deep RL Resources
    • Contributing
    • Motivation
    • Help
    • Contact
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  1. 🔧Development
  2. Modular Design

Algorithm Taxonomy

PreviousModular DesignNextClass Inheritance: A2C > PPO

Last updated 3 years ago

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Algorithm Taxonomy

Deep RL algorithms can be classified into a family tree based on their methods / functions they learn, such as the one shown below.

Algorithms often extends an existing one by modifying or adding components. Most model-free algorithms are descended from SARSA and REINFORCE. The figure below shows some of the algorithms in SLM Lab, and their relationships.

Naturally, implementations can be consistent with this theoretical taxonomy by using class inheritance and modular components. This is precisely what SLM Lab does.

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Source: Foundations of Deep Reinforcement Learning, Graesser & Keng.
Source: Foundations of Deep Reinforcement Learning, Graesser & Keng.