updated usage and modifications rst

docs/source/usage.rst

@@ -5,24 +5,61 @@ Environment objects, including agents, entities and rules, that are specified in
 Using ``quickstart_use`` creates a default config-file and another one that lists all possible options of the environment.
 Also, it generates an initial script where an agent is executed in the environment specified by the config-file.
 
-The script initializes the environment, monitoring and recording of the environment, and includes the reinforcement learning loop:
+After initializing the environment using the specified configuration file, the script enters a reinforcement learning loop.
+The loop consists of episodes, where each episode involves resetting the environment, executing actions, and receiving feedback.
 
->>>     path = Path('marl_factory_grid/configs/default_config.yaml')
-        factory = Factory(path)
-        factory = EnvMonitor(factory)
-        factory = EnvRecorder(factory)
-        for episode in trange(10):
-            _ = factory.reset()
-            done = False
-            if render:
-                factory.render()
-            action_spaces = factory.action_space
-            agents = []
-            while not done:
-                a = [randint(0, x.n - 1) for x in action_spaces]
-                obs_type, _, reward, done, info = factory.step(a)
-                if render:
-                    factory.render()
-                if done:
-                    print(f'Episode {episode} done...')
-                    break
+Here's a breakdown of the key components in the provided script. Feel free to customize it based on your specific requirements:
+
+1. **Initialization:**
+
+>>> path = Path('marl_factory_grid/configs/default_config.yaml')
+    factory = Factory(path)
+    factory = EnvMonitor(factory)
+    factory = EnvRecorder(factory)
+
+    - The `path` variable points to the location of your configuration file. Ensure it corresponds to the correct path.
+    - `Factory` initializes the environment based on the provided configuration.
+    - `EnvMonitor` and `EnvRecorder` are optional components. They add monitoring and recording functionalities to the environment, respectively.
+
+2. **Reinforcement Learning Loop:**
+
+>>> for episode in trange(10):
+        _ = factory.reset()
+        done = False
+        if render:
+            factory.render()
+        action_spaces = factory.action_space
+        agents = []
+
+    - The loop iterates over a specified number of episodes (in this case, 10).
+    - `factory.reset()` resets the environment for a new episode.
+    - `factory.render()` is used for visualization if rendering is enabled.
+    - `action_spaces` stores the action spaces available for the agents.
+    - `agents` will store agent-specific information during the episode.
+
+3. **Taking Actions:**
+
+>>> while not done:
+        a = [randint(0, x.n - 1) for x in action_spaces]
+        obs_type, _, reward, done, info = factory.step(a)
+        if render:
+            factory.render()
+
+    - Within each episode, the loop continues until the environment signals completion (`done`).
+    - `a` represents a list of random actions for each agent based on their action space.
+    - `factory.step(a)` executes the actions, returning observation types, rewards, completion status, and additional information.
+
+4. **Handling Episode Completion:**
+
+>>> if done:
+        print(f'Episode {episode} done...')
+
+    - After each episode, a message is printed indicating its completion.
+
+
+Evaluating the run
+----
+
+If monitoring and recording are enabled, the environment states will be traced and recorded automatically.
+
+Plotting. At the moment a plot of the evaluation score across the different episodes is automatically generated.