Remove redundant code in tutorials

docs/code_examples/aec_rps.py

@@ -1,5 +1,3 @@
-import functools
-
 import gymnasium
 import numpy as np
 from gymnasium.spaces import Discrete
@@ -75,26 +73,17 @@ def __init__(self, render_mode=None):
             zip(self.possible_agents, list(range(len(self.possible_agents))))
         )
 
-        # optional: we can define the observation and action spaces here as attributes to be used in their corresponding methods
-        self._action_spaces = {agent: Discrete(3) for agent in self.possible_agents}
+        # we want to define the spaces as fixed objects so we can seed them
         self._observation_spaces = {
             agent: Discrete(4) for agent in self.possible_agents
         }
-        self.render_mode = render_mode
+        self._action_spaces = {agent: Discrete(3) for agent in self.possible_agents}
 
-    # Observation space should be defined here.
-    # lru_cache allows observation and action spaces to be memoized, reducing clock cycles required to get each agent's space.
-    # If your spaces change over time, remove this line (disable caching).
-    @functools.lru_cache(maxsize=None)
-    def observation_space(self, agent):
-        # gymnasium spaces are defined and documented here: https://gymnasium.farama.org/api/spaces/
-        return Discrete(4)
-
-    # Action space should be defined here.
-    # If your spaces change over time, remove this line (disable caching).
-    @functools.lru_cache(maxsize=None)
-    def action_space(self, agent):
-        return Discrete(3)
+        # observation and action spaces are defined as functions which take in an agent id
+        # and returns the relevant spaces.
+        self.observation_space = lambda agent: self._observation_spaces[agent]
+        self.action_space = lambda agent: self._action_spaces[agent]
+        self.render_mode = render_mode
 
     def render(self):
         """

docs/code_examples/parallel_rps.py

@@ -1,5 +1,3 @@
-import functools
-
 import gymnasium
 import numpy as np
 from gymnasium.spaces import Discrete
@@ -76,7 +74,6 @@ def __init__(self, render_mode=None):
         self.agent_name_mapping = dict(
             zip(self.possible_agents, list(range(len(self.possible_agents))))
         )
-        self.render_mode = render_mode
 
     # Observation space should be defined here.
     # lru_cache allows observation and action spaces to be memoized, reducing clock cycles required to get each agent's space.

tutorials/CustomEnvironment/tutorial2_adding_game_logic.py

@@ -1,4 +1,3 @@
-import functools
 import random
 from copy import copy
 
@@ -43,6 +42,13 @@ def __init__(self):
         self.timestep = None
         self.possible_agents = ["prisoner", "guard"]
 
+        self._observation_spaces = {
+            agent: MultiDiscrete([7 * 7] * 3) for agent in self.possible_agents
+        }
+        self.observation_space = lambda agent: self._observation_spaces[agent]
+        self._action_spaces = {agent: Discrete(4) for agent in self.possible_agents}
+        self.action_space = lambda agent: self._action_spaces[agent]
+
     def reset(self, seed=None, options=None):
         """Reset set the environment to a starting point.
 
@@ -162,17 +168,3 @@ def render(self):
         grid[self.guard_y, self.guard_x] = "G"
         grid[self.escape_y, self.escape_x] = "E"
         print(f"{grid} \n")
-
-    # Observation space should be defined here.
-    # lru_cache allows observation and action spaces to be memoized, reducing clock cycles required to get each agent's space.
-    # If your spaces change over time, remove this line (disable caching).
-    @functools.lru_cache(maxsize=None)
-    def observation_space(self, agent):
-        # gymnasium spaces are defined and documented here: https://gymnasium.farama.org/api/spaces/
-        return MultiDiscrete([7 * 7] * 3)
-
-    # Action space should be defined here.
-    # If your spaces change over time, remove this line (disable caching).
-    @functools.lru_cache(maxsize=None)
-    def action_space(self, agent):
-        return Discrete(4)

tutorials/CustomEnvironment/tutorial3_action_masking.py

@@ -1,4 +1,3 @@
-import functools
 import random
 from copy import copy
 
@@ -43,6 +42,13 @@ def __init__(self):
         self.timestep = None
         self.possible_agents = ["prisoner", "guard"]
 
+        self._observation_spaces = {
+            agent: MultiDiscrete([7 * 7 - 1] * 3) for agent in self.possible_agents
+        }
+        self.observation_space = lambda agent: self._observation_spaces[agent]
+        self._action_spaces = {agent: Discrete(4) for agent in self.possible_agents}
+        self.action_space = lambda agent: self._action_spaces[agent]
+
     def reset(self, seed=None, options=None):
         """Reset set the environment to a starting point.
 
@@ -198,17 +204,3 @@ def render(self):
         grid[self.guard_y, self.guard_x] = "G"
         grid[self.escape_y, self.escape_x] = "E"
         print(f"{grid} \n")
-
-    # Observation space should be defined here.
-    # lru_cache allows observation and action spaces to be memoized, reducing clock cycles required to get each agent's space.
-    # If your spaces change over time, remove this line (disable caching).
-    @functools.lru_cache(maxsize=None)
-    def observation_space(self, agent):
-        # gymnasium spaces are defined and documented here: https://gymnasium.farama.org/api/spaces/
-        return MultiDiscrete([7 * 7 - 1] * 3)
-
-    # Action space should be defined here.
-    # If your spaces change over time, remove this line (disable caching).
-    @functools.lru_cache(maxsize=None)
-    def action_space(self, agent):
-        return Discrete(4)