From 7d86dd706bc1b3e6f6583f374b66114295e6be54 Mon Sep 17 00:00:00 2001
From: Christoph Kowalski <christoph.kowalski@titus-research.eu>
Date: Wed, 16 Oct 2024 11:03:16 +0200
Subject: [PATCH] Adapted docstring
---
.../reinforcement_learning/gym_env.py | 15 +++------------
.../obs_converter/advanced_converter_array.py | 2 +-
.../reinforcement_learning/train_single_agent.py | 1 -
3 files changed, 4 insertions(+), 14 deletions(-)
diff --git a/cooperative_cuisine/reinforcement_learning/gym_env.py b/cooperative_cuisine/reinforcement_learning/gym_env.py
index 401654f2..3c77b6d7 100644
--- a/cooperative_cuisine/reinforcement_learning/gym_env.py
+++ b/cooperative_cuisine/reinforcement_learning/gym_env.py
@@ -36,7 +36,6 @@ class SimpleActionSpace(Enum):
def get_env_action(player_id, simple_action, duration):
-
"""
Args:
@@ -103,7 +102,6 @@ visualizer.set_grid_size(40)
def shuffle_counters(env):
-
"""
Shuffles the counters of an environment
Args:
@@ -160,7 +158,8 @@ class EnvGymWrapper(Env):
config_env = OmegaConf.to_container(config.environment, resolve=True)
config_item_info = OmegaConf.to_container(config.item_info, resolve=True)
for val in config_env['hook_callbacks']:
- config_env['hook_callbacks'][val]["callback_class"] = instantiate(config_env['hook_callbacks'][val]["callback_class"])
+ config_env['hook_callbacks'][val]["callback_class"] = instantiate(
+ config_env['hook_callbacks'][val]["callback_class"])
config_env["orders"]["order_gen_class"] = instantiate(config_env["orders"]["order_generator"])
self.config_env = config_env
self.config_item_info = config_item_info
@@ -223,6 +222,7 @@ class EnvGymWrapper(Env):
and additional information
"""
# this is simply a work-around to enable no action which is necessary for the play_gym.py
+ # but not for the rl agent
if action == 8:
observation = self.get_observation()
reward = self.env.score - self.prev_score
@@ -240,19 +240,14 @@ class EnvGymWrapper(Env):
self.env.step(
timedelta(seconds=self.global_step_time / self.in_between_steps)
)
-
observation = self.get_observation()
-
reward = self.env.score - self.prev_score
self.prev_score = self.env.score
-
if reward > 0:
print("- - - - - - - - - - - - - - - - SCORED", reward)
-
terminated = self.env.game_ended
truncated = self.env.game_ended
info = {}
-
return observation, reward, terminated, truncated, info
def reset(self, seed=None, options=None):
@@ -272,16 +267,12 @@ class EnvGymWrapper(Env):
if self.randomize_counter_placement:
shuffle_counters(self.env)
-
self.player_name = str(0)
self.env.add_player(self.player_name)
self.player_id = list(self.env.players.keys())[0]
-
info = {}
obs = self.get_observation()
-
self.prev_score = 0
-
return obs, info
def get_observation(self):
diff --git a/cooperative_cuisine/reinforcement_learning/obs_converter/advanced_converter_array.py b/cooperative_cuisine/reinforcement_learning/obs_converter/advanced_converter_array.py
index be7dbcad..e4bb5f65 100644
--- a/cooperative_cuisine/reinforcement_learning/obs_converter/advanced_converter_array.py
+++ b/cooperative_cuisine/reinforcement_learning/obs_converter/advanced_converter_array.py
@@ -52,7 +52,7 @@ class AdvancedStateConverterArray(StateToObservationConverter):
setup is chosen.
- Returns: An encoding for the environment state that is not onehot
+ Returns: An encoding for the environment state
"""
if player is not None:
diff --git a/cooperative_cuisine/reinforcement_learning/train_single_agent.py b/cooperative_cuisine/reinforcement_learning/train_single_agent.py
index d78e0f74..0b9647be 100644
--- a/cooperative_cuisine/reinforcement_learning/train_single_agent.py
+++ b/cooperative_cuisine/reinforcement_learning/train_single_agent.py
@@ -27,7 +27,6 @@ def main(cfg: DictConfig):
config: dict[str, Any] = OmegaConf.to_container(cfg.model, resolve=True)
env_info: dict[str, Any] = OmegaConf.to_container(cfg.environment, resolve=True)
debug: bool = additional_configs["debug_mode"]
-
vec_env = additional_configs["vec_env"]
number_envs_parallel = config["number_envs_parallel"]
model_class = instantiate(cfg.model.model_type)
--
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