SLM Lab
Search…
SLM Lab
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
Memory
Net
Profiling SLM Lab
📖Publications and Talks
Book: Foundations of Deep Reinforcement Learning
Talks and Presentations
🤓Resources
Deep RL Resources
Contributing
Motivation
Help
Contact
Powered By GitBook
Train: REINFORCE CartPole

​
🚀
 Train Mode

This tutorial will look at how to train an agent in SLM Lab, then use the automatically-saved model files to replay it in enjoy mode, using REINFORCE on CartPole.
REINFORCE is a very basic policy gradient algorithm, and we are training it on an easy environment. We will use the spec file at slm_lab/spec/benchmark/reinforce/reinforce_cartpole.json, in particular the reinforce_cartpole spec, and run it in train mode. The lab command is:
1
python run_lab.py slm_lab/spec/benchmark/reinforce/reinforce_cartpole.json reinforce_cartpole train
Copied!
This will run a Trial with 4 Sessions of different random seeds to average the results. Wait for it to run until completion, which should take about 10-20 minutes. Meanwhile, check the metrics logged in the terminal. In particular, the total_reward and its moving average total_reward_ma (with a window of 100 episodes) should climb up gradually.
REINFORCE successfully trains on CartPole when the total_reward_ma reaches close to the maximum of 200, although a score of over 100 will do for this tutorial.
When the trial completes, all the metrics, graphs and data will be saved to a timestamped folder, let's say data/reinforce_cartpole_2020_04_13_232521/. Among other things, SLM Lab also automatically saves the final and the best model files in the model folder. The model files can be used for easy playback in enjoy mode.
Next, we look at how to resume training, and to run a trained model in enjoy mode.
🚀Using SLM Lab - Previous
Lab Organization
Next - 🚀Using SLM Lab
Resume and Enjoy: REINFORCE CartPole
Last modified 6mo ago
Copy link