From 864cdf6f71e6cad189603619ff60df557f50537c Mon Sep 17 00:00:00 2001 From: kengz Date: Mon, 27 May 2019 00:52:08 -0700 Subject: [PATCH 1/2] format README titles --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 05aeff763..2e034ed95 100644 --- a/README.md +++ b/README.md @@ -49,7 +49,7 @@ SLM Lab integrates with multiple environment offerings: *Contributions are welcome to integrate more environments!* -#### [Metrics and Experimentation](#experimentation-framework) +### [Metrics and Experimentation](#experimentation-framework) To facilitate better RL development, SLM Lab also comes with prebuilt *metrics* and *experimentation framework*: - every run generates metrics, graphs and data for analysis, as well as spec for reproducibility @@ -75,7 +75,7 @@ To facilitate better RL development, SLM Lab also comes with prebuilt *metrics* ## Quick Start -### DQN CartPole +#### DQN CartPole Everything in the lab is ran using a `spec file`, which contains all the information for the run to be reproducible. These are located in `slm_lab/spec/`. @@ -103,7 +103,7 @@ This will run a new `Trial` in *training mode*. At the end of it, all the metric ![](https://kengz.gitbooks.io/slm-lab/content/assets/demo_training.png) -### A2C Atari +#### A2C Atari Run A2C to solve Atari Pong: @@ -121,7 +121,7 @@ Below shows a trial graph with multiple sessions: ![](https://kengz.gitbooks.io/slm-lab/content/assets/demo_atari_graph.png) -### Benchmark +#### Benchmark To run a full benchmark, simply pick a file and run it in train mode. For example, for A2C Atari benchmark, the spec file is `slm_lab/spec/benchmark/a2c/a2c_atari.json`. This file is parametrized to run on a set of environments. Run the benchmark: @@ -131,7 +131,7 @@ python run_lab.py slm_lab/spec/benchmark/a2c/a2c_atari.json a2c_atari train This will spawn multiple processes to run each environment in its separate `Trial`, and the data is saved to `data/` as usual. -### Experimentation / Hyperparameter search +#### Experimentation / Hyperparameter search An [`Experiment`](https://github.com/kengz/SLM-Lab/blob/master/slm_lab/experiment/control.py) is a hyperparameter search, which samples multiple `spec`s from a search space. `Experiment` spawns a `Trial` for each `spec`, and each `Trial` runs multiple duplicated `Session`s for averaging its results. From 45e24ee820692976a82204de67b4e4a0b5388d49 Mon Sep 17 00:00:00 2001 From: kengz Date: Mon, 27 May 2019 00:54:00 -0700 Subject: [PATCH 2/2] update package json --- package.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/package.json b/package.json index f734ff64c..1c89dc117 100644 --- a/package.json +++ b/package.json @@ -1,6 +1,6 @@ { "name": "slm_lab", - "version": "2.1.2", + "version": "4.0.0", "description": "Modular Deep Reinforcement Learning framework in PyTorch.", "main": "index.js", "scripts": {