Merge branch 'main' of github.com:linqs/pacman

Author: eriq-augustine

pacai/__init__.py

@@ -2,4 +2,4 @@
 The pacai package is the top-level package for the Pacman AI project maintained by EduLinq, Eriq Augustine, and the LINQS lab at UCSC.
 """
 
-__version__ = '2.0.0'
+__version__ = '2.0.1'

pacai/agents/minimax.py

@@ -73,7 +73,7 @@ def get_action(self, state: pacai.core.gamestate.GameState) -> pacai.core.action
         self._stats_states_evaluated.append(0)
         self._stats_nodes_visited.append(0)
 
-        actions, score = self.minimax_step(state, self.ply_count + 1, math.inf, -math.inf)
+        actions, score = self.minimax_step(state, self.ply_count + 1, -math.inf, math.inf)
         action = self.rng.choice(actions)
 
         logging.debug("Turn: %d, Game State Score: %d, Minimax Score: %d, Chosen Action: %s, States Evaluated: %d, Nodes Visited: %d.",

pacai/core/board.py

@@ -164,7 +164,7 @@ def apply_action(self, action: pacai.core.action.Action) -> 'Position':
 
         return self.add(offset)
 
-    def __lt__(self, other: 'Position') -> bool:
+    def __lt__(self, other: 'Position') -> bool:  # type: ignore[override]
         return (self._hash < other._hash)
 
     def __eq__(self, other: object) -> bool:

pacai/core/mdp.py

@@ -54,7 +54,7 @@ def __init__(self,
     def is_terminal(self) -> bool:
         return (self.position == TERMINAL_POSITION)
 
-    def __lt__(self, other: 'MDPStatePosition') -> bool:
+    def __lt__(self, other: 'MDPStatePosition') -> bool:  # type: ignore[override]
         return (self.position < other.position)
 
     def __eq__(self, other: object) -> bool:

pacai/gridworld/mdp.py

@@ -132,7 +132,7 @@ def get_transitions(self, state: pacai.core.mdp.MDPStatePosition, action: pacai.
         if (action not in possible_actions):
             raise ValueError(f"Got an illegal action '{action}'. Available actions are: {possible_actions}.")
 
-        (north_state, east_state, south_state, west_state) = self._get_move_states(state)
+        (north_state, east_state, south_state, west_state) = self._get_move_states(state)  # pylint: disable=unbalanced-tuple-unpacking
 
         transitions = []
 

pacai/student/multiagents.py

@@ -41,7 +41,7 @@ class MyMinimaxLikeAgent(pacai.agents.minimax.MinimaxLikeAgent):
     (you can ignore alpha and beta).
 
     To implement minimax with alpha-beta pruning,
-    minimax_step_max() and minimax_step_min() with alpha an beta are required.
+    minimax_step_max() and minimax_step_min() with alpha and beta are required.
 
     To implement expectimax, minimax_step_max() and minimax_step_expected_min() are required.