diff --git a/docs/getting_started.md b/docs/getting_started.md
index 83b33e210..be3f36f37 100644
--- a/docs/getting_started.md
+++ b/docs/getting_started.md
@@ -66,7 +66,7 @@ This algorithm works by building up a sequence of button presses that do well in
 
 Using ["Proximal Policy Optimization"](https://arxiv.org/abs/1707.06347) by Schulman et al., you can train an agent to play many of the games, though it takes awhile and is much faster with a GPU.
 
-This example requires installing [OpenAI Baselines](https://github.com/openai/baselines).  Once installed, you can run it:
+This example requires installing [Stable Baselines](https://github.com/DLR-RM/stable-baselines3).  Once installed, you can run it:
 
 ```shell
 python3 -m retro.examples.ppo --game Airstriker-Genesis
diff --git a/retro/examples/trivial_random_agent.py b/retro/examples/trivial_random_agent.py
index 48ee02d30..dc2f64e60 100644
--- a/retro/examples/trivial_random_agent.py
+++ b/retro/examples/trivial_random_agent.py
@@ -5,9 +5,10 @@ def main():
     env = retro.make(game="Airstriker-Genesis")
     env.reset()
     while True:
-        obs, rew, done, info = env.step(env.action_space.sample())
+        action = env.action_space.sample()
+        observation, reward, terminated, truncated, info = env.step(action)
         env.render()
-        if done:
+        if terminated or truncated:
             env.reset()
     env.close()