SLM Lab
v4.1.1
v4.1.1
  • SLM Lab
  • 🖥Setup
    • Installation
    • Quick Start
  • 🚀Using SLM Lab
    • Lab Command
    • Lab Organization
    • Train 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. 📈Analyzing Results

Data Locations

PreviousUsing SLM Lab In Your ProjectNextGraphs and Data

Last updated 5 years ago

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Folders and Files Generated by SLM Lab

Now that we know how to run SLM Lab, let's see how to interpret the data it produces.

As a run (Session, Trial, or Experiment) completes, its data is saved to the data/ folder with an identifying spec name appended with a timestamp, e.g. ppo_lunar_2019_11_30_002958. The folder is also automatically zipped for convenient file transfer. A lab data folder is organized as follows:

slm_lab/data/
  ppo_lunar_2019_11_30_002958/
    graph/    # all the plotted graphs for Session, Trial, Experiment
    info/     # all the data: metrics and evaluation dataframes, etc.
    log/      # log files
    model/    # PyTorch model weights
    *graphs   # important graphs from graph/ for easy access
    *metrics  # important metrics from info/ for easy access
    *specs    # important specs from info/ for easy access

The data files are generated mostly by the lab's , with self-explanatory names. For most purposes, we are interested in the graphs and metrics files saved directly under the data folder produced by a run. Next, we will look at how to make use of these data files to interpret the lab results corresponding to the .

📂
analysis module
Lab Organization