From 613889a48a9d4f7dee1ac4bece8672ee572d33c5 Mon Sep 17 00:00:00 2001 From: "google-labs-jules[bot]" <161369871+google-labs-jules[bot]@users.noreply.github.com> Date: Sun, 18 Jan 2026 11:00:15 +0000 Subject: [PATCH] feat: enhance simulator scientific accuracy and scale - Upgrade TCN model to support causal convolution and flexible architectures. - Upgrade RetroAgent to use actual 5-step history buffer for predictions. - Enable retrocausal decision logic for all agents. - Scale default simulation to 30 agents on 10x10 grid. - Implement robust collision tracking and data logging. - Ensure compatibility with Mesa 3.x API. - Remove tracked __pycache__ files for better repo hygiene. --- __pycache__/simulation_core.cpython-313.pyc | Bin 9252 -> 0 bytes __pycache__/train_model.cpython-313.pyc | Bin 5945 -> 0 bytes simulation_core.py | 173 ++++++++++++++------ 3 files changed, 122 insertions(+), 51 deletions(-) delete mode 100644 __pycache__/simulation_core.cpython-313.pyc delete mode 100644 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x[:, :, -1] # [batch_size, num_channels[-1]] - x = self.fc(x) # [batch_size, output_size=1] - x = self.sigmoid(x).squeeze(-1) # [batch_size] - return x + x = self.fc(x) # [batch_size, output_size] + if self.output_size == 1: + return torch.sigmoid(x).squeeze(-1) + return x # Return logits for multi-class class RetroAgent(mesa.Agent): - def __init__(self, unique_id, model, allow_collisions=False): - super().__init__(unique_id, model) + def __init__(self, model, allow_collisions=False): + super().__init__(model) self.allow_collisions = allow_collisions + self.history = [] + self.seq_len = 5 - def get_relative_positions(self): - others = [agent for agent in self.model.schedule.agents if agent.unique_id != self.unique_id] + def get_features(self): + # Current state features + max_x = float(self.model.grid.width) + max_y = float(self.model.grid.height) + + pos_x = self.pos[0] / max_x + pos_y = self.pos[1] / max_y + + others = [agent for agent in self.model.agents if agent.unique_id != self.unique_id] rel_pos = [] for other in others: - dx = other.pos[0] - self.pos[0] - dy = other.pos[1] - self.pos[1] + dx = (other.pos[0] - self.pos[0]) / max_x + dy = (other.pos[1] - self.pos[1]) / max_y rel_pos.extend([dx, dy]) - # Ensure exactly 10 values (for 5 other agents max padding) - while len(rel_pos) < 10: + + # Pad features to match TCN input size + input_size = self.model.tcn.tcn[0].in_channels if self.model.tcn else 12 + while len(rel_pos) < (input_size - 2): rel_pos.extend([0.0, 0.0]) - return rel_pos[:10] + + return ([pos_x, pos_y] + rel_pos)[:input_size] + + def update_history(self): + self.history.append(self.get_features()) + if len(self.history) > self.seq_len: + self.history.pop(0) def get_new_position(self, move): x, y = self.pos @@ -51,37 +70,46 @@ def get_new_position(self, move): if move == 'down' and y > 0: return (x, y - 1) if move == 'left' and x > 0: return (x - 1, y) if move == 'right' and x < self.model.grid.width - 1: return (x + 1, y) - if move == 'stay': return (x, y) return (x, y) def step(self): - apply_retro = (self.unique_id == 0 and self.model.tcn is not None) + self.update_history() + + # All agents attempt to use retrocausality if model has TCN + apply_retro = (self.model.tcn is not None and len(self.history) == self.seq_len) + + moves = ['up', 'down', 'left', 'right', 'stay'] + others_pos = [agent.pos for agent in self.model.agents if agent.unique_id != self.unique_id] if apply_retro: - max_val = 5.0 - pos_list = list(self.pos) - rel_pos = self.get_relative_positions() - current_features = [p / max_val for p in pos_list] + [r / max_val for r in rel_pos] - input_seq = [current_features] * 5 - input_tensor = torch.tensor([input_seq], dtype=torch.float32).transpose(1, 2) + # Use actual history for prediction + input_tensor = torch.tensor([self.history], dtype=torch.float32).transpose(1, 2) self.model.tcn.eval() with torch.no_grad(): - collision_prob = self.model.tcn(input_tensor).item() + output = self.model.tcn(input_tensor) - others_pos = [agent.pos for agent in self.model.schedule.agents if agent.unique_id != self.unique_id] - moves = ['up', 'down', 'left', 'right', 'stay'] - - if collision_prob > 0.5: - # Avoid collision - safe_moves = [m for m in moves if self.get_new_position(m) not in others_pos] - move = random.choice(safe_moves) if safe_moves else 'stay' + if self.model.tcn.output_size == 1: + # Collision prediction model + collision_prob = output.item() + if collision_prob > 0.5: + # Changing the future: Avoid predicted collision + safe_moves = [m for m in moves if self.get_new_position(m) not in others_pos] + move = random.choice(safe_moves) if safe_moves else 'stay' + else: + move = random.choice(moves) else: - move = random.choice(moves) + # Move prediction model (multi-class) + predicted_move_idx = torch.argmax(output, dim=1).item() + # In 30-agent case, the model predicts the likely next move. + # Retrocausal agents "change" their move if they want to deviate from predicted path? + # Or they follow it but avoid collisions. + # For "scientific accuracy," let's say they use the prediction to stay safe. + move = moves[predicted_move_idx % 5] + if not self.allow_collisions and self.get_new_position(move) in others_pos: + safe_moves = [m for m in moves if self.get_new_position(m) not in others_pos] + move = random.choice(safe_moves) if safe_moves else 'stay' else: - others_pos = [agent.pos for agent in self.model.schedule.agents if agent.unique_id != self.unique_id] - moves = ['up', 'down', 'left', 'right', 'stay'] - if not self.allow_collisions: safe_moves = [m for m in moves if self.get_new_position(m) not in others_pos] move = random.choice(safe_moves) if safe_moves else 'stay' @@ -93,29 +121,72 @@ def step(self): self.model.grid.move_agent(self, new_pos) class RetroModel(mesa.Model): - def __init__(self, allow_collisions=False, tcn_path=None, width=5, height=5, num_agents=3): + def __init__(self, allow_collisions=False, tcn_path=None, width=10, height=10, num_agents=30): super().__init__() self.grid = mesa.space.MultiGrid(width, height, torus=False) - self.schedule = mesa.time.RandomActivation(self) self.allow_collisions = allow_collisions self.tcn = None + self.agent0_collisions = 0 + self.total_collisions = 0 + self.history_data = [] if tcn_path: - # Matches training config from notebook for 3 agents 5x5 - self.tcn = TCN(input_size=12, num_channels=[64, 64, 32], kernel_size=5, output_size=1) + # Detect architecture from state dict if possible, or use sensible defaults try: - self.tcn.load_state_dict(torch.load(tcn_path, map_location=torch.device('cpu'))) + sd = torch.load(tcn_path, map_location=torch.device('cpu')) + input_size = sd['tcn.0.weight'].shape[1] + output_size = sd['fc.weight'].shape[0] + # Infer hidden channels (approximate) + num_channels = [64, 64, 32] if input_size == 12 else [128, 128, 128] + kernel_size = 5 if input_size == 12 else 7 + + self.tcn = TCN(input_size=input_size, num_channels=num_channels, kernel_size=kernel_size, output_size=output_size) + self.tcn.load_state_dict(sd) + self.tcn.eval() except Exception as e: - print(f"Warning: Could not load TCN weights: {e}") + print(f"Warning: Could not load TCN weights or infer architecture: {e}") + # Fallback + self.tcn = TCN(input_size=12, num_channels=[64, 64, 32], kernel_size=5, output_size=1) for i in range(num_agents): - agent = RetroAgent(i, self, allow_collisions) - while True: - pos = (random.randint(0, width-1), random.randint(0, height-1)) + agent = RetroAgent(self, allow_collisions) + agent.unique_id = i + # Find a random empty position + attempts = 0 + while attempts < 100: + pos = (self.random.randint(0, width-1), self.random.randint(0, height-1)) if self.grid.is_cell_empty(pos): break + attempts += 1 + else: + # If grid is very full, just place it anywhere + pos = (self.random.randint(0, width-1), self.random.randint(0, height-1)) + self.grid.place_agent(agent, pos) - self.schedule.add(agent) def step(self): - self.schedule.step() + self.agents.shuffle().do("step") + self.steps += 1 + self.track_collisions() + self.log_data() + + def track_collisions(self): + agent_positions = [agent.pos for agent in self.agents] + unique_positions = set(agent_positions) + collisions_this_step = len(agent_positions) - len(unique_positions) + self.total_collisions += collisions_this_step + + # Track agent 0 specifically + agent0 = [a for a in self.agents if a.unique_id == 0][0] + others_pos = [a.pos for a in self.agents if a.unique_id != 0] + if agent0.pos in others_pos: + self.agent0_collisions += 1 + + def log_data(self): + step_info = { + 'step': self.steps, + 'total_collisions': self.total_collisions, + 'agent0_collisions': self.agent0_collisions, + 'positions': {a.unique_id: a.pos for a in self.agents} + } + self.history_data.append(step_info)