From 93b9625c95dfa5e29458b5405b1e835291ba226f Mon Sep 17 00:00:00 2001
From: Tonio Weidler <web@tonioweidler.de>
Date: Thu, 21 Jul 2022 16:45:54 +0200
Subject: [PATCH 1/2] Update README.md

---
 README.md | 14 --------------
 1 file changed, 14 deletions(-)

diff --git a/README.md b/README.md
index 068de023..6294d9c7 100644
--- a/README.md
+++ b/README.md
@@ -72,24 +72,10 @@ from angorapy.models import get_model_builder
 
 wrappers = [StateNormalizationTransformer, RewardNormalizationTransformer]
 env = make_env("LunarLanderContinuous-v2", reward_config=None, transformers=wrappers)
-
-# make policy distribution
 distribution = BetaPolicyDistribution(env)
-
-# the agent needs to create the model itself, so we build a method that builds a model
 build_models = get_model_builder(model="simple", model_type="ffn", shared=False)
-
-# given the model builder and the environment we can create an agent
 agent = PPOAgent(build_models, env, horizon=1024, workers=12, distribution=distribution)
-
-# let's check the agents ID, so we can find its saved states after training
-print(f"My Agent's ID: {agent.agent_id}")
-
-# ... and then train that agent for n cycles
 agent.drill(n=100, epochs=3, batch_size=64)
-
-# after training, we can save the agent for analysis or the like
-agent.save_agent_state()
 ```
 
 For more details, consult the [examples](examples).

From 9c88c2cc8ac7cdbfc8e3a53eac1ed6a7e4e8e6b2 Mon Sep 17 00:00:00 2001
From: Tonio Weidler <web@tonioweidler.de>
Date: Thu, 21 Jul 2022 16:52:35 +0200
Subject: [PATCH 2/2] Update README.md

---
 README.md | 14 +++++---------
 1 file changed, 5 insertions(+), 9 deletions(-)

diff --git a/README.md b/README.md
index 6294d9c7..91b65d51 100644
--- a/README.md
+++ b/README.md
@@ -64,18 +64,14 @@ Where ID is the agent's ID given when its created (`train.py` prints this outt,
 To train agents with custom models, environments, etc. you write your own script. The following is a minimal example:
 
 ```python
-from angorapy.agent.ppo_agent import PPOAgent
-from angorapy.common.policies import BetaPolicyDistribution
-from angorapy.common.transformers import RewardNormalizationTransformer, StateNormalizationTransformer
 from angorapy.common.wrappers import make_env
 from angorapy.models import get_model_builder
+from angorapy.agent.ppo_agent import PPOAgent
 
-wrappers = [StateNormalizationTransformer, RewardNormalizationTransformer]
-env = make_env("LunarLanderContinuous-v2", reward_config=None, transformers=wrappers)
-distribution = BetaPolicyDistribution(env)
-build_models = get_model_builder(model="simple", model_type="ffn", shared=False)
-agent = PPOAgent(build_models, env, horizon=1024, workers=12, distribution=distribution)
-agent.drill(n=100, epochs=3, batch_size=64)
+env = make_env("LunarLanderContinuous-v2")
+model_builder = get_model_builder("simple", "ffn")
+agent = PPOAgent(model_builder, env)
+agent.drill(100, 10, 512)
 ```
 
 For more details, consult the [examples](examples).