[README] Improve cold-start description.
Add link to getting started guide, and improve comments on the code
sample.
@@ -32,7 +32,11 @@ developers to expose new optimization problems for AI.
# Getting Started
-Starting with CompilerGym is simple if you are familiar with other gym environments.
+Starting with CompilerGym is simple. If you not already familiar with the gym
+interface, refer to the
+[getting started guide](http://facebookresearch.github.io/CompilerGym/getting_started.html)
+for an overview of the key concepts.
+
## Installation
@@ -84,15 +88,16 @@ environment using:
## Trying it out
-To use the environments, import `compiler_gym`:
+In Python, import `compiler_gym` to use the environments:
```py
>>> import gym
->>> import compiler_gym
->>> env = gym.make("llvm-autophase-ic-v0")
->>> env.require_dataset("npb-v0") # download a dataset of benchmarks
->>> env.reset() # calling reset starts a new compilation episode
->>> env.step(env.action_space.sample()) # apply a random optimization
+>>> import compiler_gym # imports the CompilerGym environments
+>>> env = gym.make("llvm-autophase-ic-v0") # starts a new environment
+>>> env.require_dataset("npb-v0") # downloads a set of programs
+>>> env.reset() # starts a new compilation session with a random program
+>>> env.render() # prints the IR of the program
+>>> env.step(env.action_space.sample()) # applies a random optimization, updates state/reward/actions
```
See the
