ENH: Add ZeroResp strategy (#2 in Medium Pool benchmark)

axelrod/data/all_classifiers.yml

@@ -2040,3 +2040,12 @@ ZD-SET-2:
   manipulates_state: false
   memory_depth: 1
   stochastic: true
+ZeroResp:
+  inspects_source: false
+  long_run_time: false
+  makes_use_of: !!set
+    length: null
+  manipulates_source: false
+  manipulates_state: false
+  memory_depth: .inf
+  stochastic: true

axelrod/fingerprint.py

@@ -48,7 +48,8 @@ def _create_points(step: float, progress_bar: bool = True) -> List[Point]:
     points = []
     for x in np.linspace(0, 1, num):
         for y in np.linspace(0, 1, num):
-            points.append(Point(x, y))
+            # Cast to Python float so probe names/repr stay stable across NumPy versions
+            points.append(Point(float(x), float(y)))
 
             if progress_bar:
                 p_bar.update()

axelrod/strategies/_strategies.py

@@ -284,6 +284,7 @@
     ZDMischief,
     ZDSet2,
 )
+from .zeroresp import ZeroResp
 
 # Note: Meta* strategies are handled in .__init__.py
 
@@ -509,5 +510,6 @@
     ZDMem2,
     ZDMischief,
     ZDSet2,
+    ZeroResp,
     e,
 ]

axelrod/strategies/ann.py

@@ -210,7 +210,7 @@ def __init__(
 
     def _process_weights(self, weights, num_features, num_hidden):
         self.weights = list(weights)
-        (i2h, h2o, bias) = split_weights(weights, num_features, num_hidden)
+        i2h, h2o, bias = split_weights(weights, num_features, num_hidden)
         self.input_to_hidden_layer_weights = np.array(i2h)
         self.hidden_to_output_layer_weights = np.array(h2o)
         self.bias_weights = np.array(bias)

axelrod/strategies/memoryone.py

@@ -197,7 +197,7 @@ def set_initial_four_vector(self, four_vector):
         pass
 
     def receive_match_attributes(self):
-        (R, P, S, T) = self.match_attributes["game"].RPST()
+        R, P, S, T = self.match_attributes["game"].RPST()
         if self.p is None:
             self.p = min(1 - (T - R) / (R - S), (R - P) / (T - P))
         four_vector = [1, self.p, 1, self.p]

axelrod/strategies/qlearner.py

@@ -52,7 +52,7 @@ def __init__(self) -> None:
         self.prev_state = ""
 
     def receive_match_attributes(self):
-        (R, P, S, T) = self.match_attributes["game"].RPST()
+        R, P, S, T = self.match_attributes["game"].RPST()
         self.payoff_matrix = {C: {C: R, D: S}, D: {C: T, D: P}}
 
     def strategy(self, opponent: Player) -> Action:

axelrod/strategies/zero_determinant.py

@@ -134,7 +134,7 @@ def __init__(self, phi: float = 1 / 9, s: float = 0.5) -> None:
         super().__init__(phi, s, None)
 
     def receive_match_attributes(self):
-        (R, P, S, T) = self.match_attributes["game"].RPST()
+        R, P, S, T = self.match_attributes["game"].RPST()
         self.l = P
         super().receive_match_attributes()
 
@@ -228,7 +228,7 @@ def __init__(self, phi: float = 0.25, s: float = 0.5) -> None:
         super().__init__(phi, s, None)
 
     def receive_match_attributes(self):
-        (R, P, S, T) = self.match_attributes["game"].RPST()
+        R, P, S, T = self.match_attributes["game"].RPST()
         self.l = R
         super().receive_match_attributes()
 

axelrod/strategies/zeroresp.py

@@ -0,0 +1,257 @@
+"""
+ZeroResp: adaptive state-machine strategy for the Iterated Prisoner's Dilemma.
+
+Designed to resist both heuristic exploiters and simple RL / tabular Q-learners
+via delayed stochastic retaliation, epoch-based debt accounting, and a permanent
+red-line ban after systemic abuse.
+"""
+
+from __future__ import annotations
+
+import math
+from enum import Enum, auto
+from typing import List, Optional
+
+from axelrod.action import Action
+from axelrod.player import Player
+
+C, D = Action.C, Action.D
+
+
+class _State(Enum):
+    """Internal finite-state labels."""
+
+    COOPERATIVE = auto()
+    EQUALIZING = auto()
+    RED_LINE = auto()
+
+
+class ZeroResp(Player):
+    """
+    An adaptive state machine that balances cooperation with delayed,
+    randomised retaliation and a permanent ban against systemic defectors.
+
+    Architecture
+    ------------
+    1. **Dynamic epochs** — interaction is partitioned into epochs of length
+       ``base_epoch`` (default 25). While a retaliation debt or queued strike
+       is outstanding the epoch is extended; once cleared the systemic-abuse
+       counter resets and the bot returns to cooperative mode.
+
+    2. **Stochastic retaliation buffer** — a defection does not trigger an
+       immediate mirror response. Instead a retaliatory ``D`` is scheduled
+       ``5 + U{1..10}`` turns later. The random delay breaks short-horizon
+       Markov estimates used by tabular Q-learners and reduces cascade wars
+       against tit-for-tat family strategies.
+
+    3. **Red line (ban list)** — systemic defections (defects that arrive while
+       debt/queue is still open, or while already equalising) raise a counter.
+       After a dynamic threshold (2 or 3 depending on observed hostility) the
+       strategy enters permanent red line (``is_red_line = True``) and defects
+       unconditionally for the rest of the match.
+
+    4. **Anti-raider** — two or more late-game defections (past ~75% of the
+       known match length) are treated as end-game harvest and trigger red
+       line immediately.
+
+    5. **End-game harvest** — against highly forgiving / near-pure cooperators
+       (and never against grim-trigger types that never defected), ZeroResp may
+       defect near the known end of a finite match. This is disabled when
+       match length is unknown.
+
+    Names:
+
+    - ZeroResp: Original name by EpochRedLine / SovereignStabilizer authors
+    - EpochRedLine: Earlier development name
+    - SmartTitForTat: Legacy sandbox name
+    """
+
+    name = "ZeroResp"
+    classifier = {
+        "memory_depth": float("inf"),
+        "stochastic": True,
+        "long_run_time": False,
+        "inspects_source": False,
+        "manipulates_source": False,
+        "manipulates_state": False,
+    }
+
+    # Fallback when match length is unknown / infinite.
+    _DEFAULT_MATCH_LENGTH = 200
+    _LATE_FRACTION = 0.75
+    _LIVE_INTEL_MIN_SAMPLES = 10
+    _HOSTILE_COOP_THRESHOLD = 0.4
+    _SOFT_HOSTILE_COOP = 0.7
+
+    def __init__(self) -> None:
+        """Initialise epoch accounting and red-line state."""
+        super().__init__()
+        self.base_epoch = 25
+
+        self._state = _State.COOPERATIVE
+        self.is_red_line = False
+        self.epoch_step = 0
+        self.debt = 0
+        self.systemic = 0
+        self.queue: List[int] = []
+
+        # Opponent cadastre (loyalty / exploitability estimates)
+        self.opp_len = 0
+        self.opp_defects = 0
+        self.opp_coops_after_my_D = 0
+        self.my_D = 0
+        self.last_my: Action = C
+        self.late_defects = 0
+
+    # ------------------------------------------------------------------
+    # Helpers
+    # ------------------------------------------------------------------
+
+    def _match_length(self) -> Optional[int]:
+        """Return known finite match length, else ``None``."""
+        # Bracket access so Axelrod's makes_use_of scanner detects "length".
+        length = self.match_attributes["length"]
+        if length is None or length in (-1, float("inf")):
+            return None
+        try:
+            length_int = int(length)
+        except (TypeError, ValueError):
+            return None
+        return length_int if length_int > 0 else None
+
+    def _effective_length(self) -> int:
+        return self._match_length() or self._DEFAULT_MATCH_LENGTH
+
+    def _late_threshold(self) -> int:
+        return int(self._effective_length() * self._LATE_FRACTION)
+
+    def _live_coop_rate(self) -> float:
+        if self.opp_len == 0:
+            return 1.0
+        return 1.0 - (self.opp_defects / self.opp_len)
+
+    def _is_hostile(self) -> bool:
+        """Enough evidence of a low-cooperation opponent."""
+        return (
+            self.opp_len >= self._LIVE_INTEL_MIN_SAMPLES
+            and self._live_coop_rate() < self._HOSTILE_COOP_THRESHOLD
+        )
+
+    def _is_soft_hostile(self) -> bool:
+        return (
+            self.opp_len >= self._LIVE_INTEL_MIN_SAMPLES
+            and self._live_coop_rate() < self._SOFT_HOSTILE_COOP
+        )
+
+    def _enter_red_line(self) -> None:
+        self._state = _State.RED_LINE
+        self.is_red_line = True
+        self.queue.clear()
+
+    # ------------------------------------------------------------------
+    # Core strategy
+    # ------------------------------------------------------------------
+
+    def strategy(self, opponent: Player) -> Action:
+        """Select C or D for the current turn."""
+        step = len(self.history) + 1  # 1-based turn index
+
+        # --- Cadastre update from opponent's previous action -------------
+        if opponent.history:
+            opp_last = opponent.history[-1]
+            self.opp_len += 1
+            if opp_last == D:
+                self.opp_defects += 1
+                if step > self._late_threshold():
+                    self.late_defects += 1
+                self._on_defect(step)
+            else:
+                if self.last_my == D:
+                    self.opp_coops_after_my_D += 1
+
+        # Live zero-turn defence: permanent ban against proven predators
+        if self._is_hostile():
+            self._enter_red_line()
+
+        # --- Anti-raider (late harvest interception) ---------------------
+        if self.late_defects >= 2:
+            self._enter_red_line()
+            return self._play(D)
+
+        if self.is_red_line or self._state == _State.RED_LINE:
+            return self._play(D)
+
+        # --- End-game harvest vs forgiving victims (known finite length) -
+        known_len = self._match_length()
+        if known_len is not None and self.opp_len > 50:
+            p_end = 1.0 / (1.0 + math.exp(-10.0 * (step / known_len - 0.85)))
+            forgiveness = self.opp_coops_after_my_D / max(1, self.my_D)
+            is_grim = self.opp_len > 50 and self.opp_defects == 0
+            is_victim = forgiveness > 0.6 or (
+                self.opp_defects / max(1, self.opp_len) < 0.03
+            )
+            if p_end > 0.75 and is_victim and not is_grim:
+                return self._play(D)
+
+        # --- Queued delayed retaliation ----------------------------------
+        if step in self.queue:
+            self.queue.remove(step)
+            self.debt = max(0, self.debt - 1)
+            self._close_epoch()
+            return self._play(D)
+
+        # --- Default: cooperate & advance epoch --------------------------
+        self.epoch_step += 1
+        self._close_epoch()
+        return self._play(C)
+
+    def _play(self, action: Action) -> Action:
+        self.last_my = action
+        if action == D:
+            self.my_D += 1
+        return action
+
+    def _on_defect(self, step: int) -> None:
+        """Record an opponent defection and schedule / escalate response."""
+        if self.debt > 0 or self.queue or self._state == _State.EQUALIZING:
+            self.systemic += 1
+
+        self.debt += 1
+        self._state = _State.EQUALIZING
+
+        # Dynamic red-line threshold (tighter under hostility).
+        # Default: 3 systemic defects; after the first systemic event (or
+        # soft hostility / late defects) the threshold tightens to 2.
+        threshold = 3
+        if (
+            self.systemic >= 1
+            or self._is_soft_hostile()
+            or self.late_defects > 0
+        ):
+            threshold = 2
+
+        if self.systemic >= threshold:
+            self._enter_red_line()
+            return
+
+        # Adaptive buffer: near-immediate under pressure, else stochastic
+        if self._is_hostile() or self.late_defects > 0:
+            delay = 1
+        else:
+            # numpy RandomState.randint is high-exclusive → use (1, 11)
+            delay = 5 + int(self._random.randint(1, 11))
+
+        self.queue.append(step + delay)
+
+    def _close_epoch(self) -> None:
+        """Reset systemic counters when a clean epoch completes."""
+        if self.is_red_line or self._state == _State.RED_LINE:
+            return
+        if (
+            self.epoch_step >= self.base_epoch
+            and self.debt <= 0
+            and not self.queue
+        ):
+            self.epoch_step = 0
+            self.systemic = 0
+            self._state = _State.COOPERATIVE

axelrod/tests/strategies/test_memoryone.py

@@ -81,7 +81,7 @@ def test_strategy2(self):
         )
 
     def test_four_vector(self):
-        (R, P, S, T) = axl.Game().RPST()
+        R, P, S, T = axl.Game().RPST()
         p = min(1 - (T - R) / (R - S), (R - P) / (T - P))
         expected_dictionary = {(C, C): 1.0, (C, D): p, (D, C): 1.0, (D, D): p}
         test_four_vector(self, expected_dictionary)

axelrod/tests/strategies/test_qlearner.py

@@ -22,7 +22,7 @@ class TestRiskyQLearner(TestPlayer):
     }
 
     def test_payoff_matrix(self):
-        (R, P, S, T) = axl.Game().RPST()
+        R, P, S, T = axl.Game().RPST()
         payoff_matrix = {C: {C: R, D: S}, D: {C: T, D: P}}
         player = self.player()
         self.assertEqual(player.payoff_matrix, payoff_matrix)