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. 🖥Setup

Quick Start

To test the installation.

PreviousInstallationNextLab Command

Last updated 5 years ago

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DQN On CartPole

This quick demo will test that the installation works. We will run DQN on the CartPole environment. For now, don't worry about the details of the Python command, as we will walk through them in a .

conda activate lab
python run_lab.py slm_lab/spec/demo.json dqn_cartpole dev

This will run a session that trains a DQN agent on the CartPole-v0 environment. Since the command above specifies dev mode, it enables verbose logging and environment rendering, which should be similar to the following screenshot:

If you let the training session run for a few minutes, you should see the CartPole getting balanced for a longer period of time. Correspondingly, the total_reward_ma should increase.

If this quick start works, then SLM Lab is ready for use.

If you encounter an issue, consult the page.

Help
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