From d5d2e7f8db90710ad98b880910913eb82c7ad1d3 Mon Sep 17 00:00:00 2001
From: Matthew Vu <mvu@college.harvard.edu>
Date: Mon, 10 Nov 2025 18:57:47 -0500
Subject: [PATCH] fix: just fixed

---
 main/__pycache__/dashboard.cpython-310.pyc    | Bin 0 -> 13016 bytes
 .../power_grid_env.cpython-310.pyc            | Bin 0 -> 13911 bytes
 .../power_grid_env.cpython-311.pyc            | Bin 0 -> 28577 bytes
 main/power_grid_env.py                        | 580 ++++++++++++++++++
 readme.md                                     | 129 ----
 5 files changed, 580 insertions(+), 129 deletions(-)
 create mode 100644 main/__pycache__/dashboard.cpython-310.pyc
 create mode 100644 main/__pycache__/power_grid_env.cpython-310.pyc
 create mode 100644 main/__pycache__/power_grid_env.cpython-311.pyc
 create mode 100644 main/power_grid_env.py

diff --git a/main/__pycache__/dashboard.cpython-310.pyc b/main/__pycache__/dashboard.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..20f54f02c56b61c2df94152da14ae95e89d71c6d
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diff --git a/main/power_grid_env.py b/main/power_grid_env.py
new file mode 100644
index 0000000..b3f7293
--- /dev/null
+++ b/main/power_grid_env.py
@@ -0,0 +1,580 @@
+"""
+Multi-Agent Power Grid Environment for Reinforcement Learning
+
+This environment simulates a 68-bus power grid with 20 agents:
+- 5 batteries (ramp rate: 50 MW/min)
+- 8 gas plants (ramp rate: 10 MW/min) 
+- 7 demand response units (ramp rate: 5 MW/min)
+
+State space: 140-dimensional (bus frequencies, generator outputs, loads)
+Each agent observes: 15-dimensional local observation
+Actions: 20 continuous power changes ΔP^i within ramp rate limits
+
+Key Features (Aligned with Proposal):
+- Correct swing equation: df/dt = P_imbalance / (2*H*S_base)
+- Total system load: 2000-5000 MW (distributed across 68 buses)
+- Communication delay: 2 seconds (SCADA delay)
+- Graduated response: exponential penalties + lenient termination (±1.5 Hz)
+- No artificial frequency clamping - realistic physics
+"""
+
+import gymnasium as gym
+from gymnasium import spaces
+import torch
+import numpy as np
+from typing import Dict, List, Tuple, Any, Optional
+import math
+import random
+
+
+class PowerGridEnv(gym.Env):
+    """Multi-agent power grid environment using gymnasium interface."""
+    
+    def __init__(self, 
+                 n_buses: int = 68,
+                 n_agents: int = 20,
+                 dt: float = 2.0/60.0,  # time step in minutes (2 seconds)
+                 frequency_bounds: Tuple[float, float] = (59.5, 60.5),
+                 load_range: Tuple[float, float] = (2000.0, 5000.0),
+                 contingency_prob: float = 0.001,
+                 device: str = 'cpu'):
+        """
+        Initialize the power grid environment.
+        
+        Args:
+            n_buses: Number of buses in the power grid (68)
+            n_agents: Number of agents (20: 5 batteries + 8 gas + 7 DR)
+            dt: Time step in minutes
+            frequency_bounds: Frequency bounds in Hz [59.5, 60.5]
+            load_range: Load range in MW [2000, 5000]
+            contingency_prob: Probability of N-1 contingency per step
+            device: PyTorch device ('cpu' or 'cuda')
+        """
+        super().__init__()
+        
+        self.device = torch.device(device)
+        self.n_buses = n_buses
+        self.n_agents = n_agents
+        self.dt = dt
+        self.frequency_bounds = frequency_bounds
+        self.load_range = load_range
+        self.contingency_prob = contingency_prob
+        
+        # Agent configuration: [batteries, gas plants, demand response]
+        self.agent_types = ['battery'] * 5 + ['gas'] * 8 + ['dr'] * 7
+        self.ramp_rates = torch.tensor([50.0] * 5 + [10.0] * 8 + [5.0] * 7, device=self.device)  # MW/min
+        
+        # Agent capacity constraints [P_min, P_max] in MW
+        self.power_min = torch.tensor([0.0] * 5 + [50.0] * 8 + [-200.0] * 7, device=self.device)
+        self.power_max = torch.tensor([100.0] * 5 + [500.0] * 8 + [0.0] * 7, device=self.device)
+        
+        # Agent-specific cost coefficients ($/MW)
+        self.cost_coefficients = torch.tensor([5.0] * 5 + [50.0] * 8 + [20.0] * 7, device=self.device)
+        
+        # Wear-and-tear coefficients for different agent types
+        self.wear_coefficients = torch.tensor([0.1] * 5 + [0.05] * 8 + [0.2] * 7, device=self.device)
+        
+        # Power system parameters
+        self.nominal_frequency = 60.0  # Hz
+        self.base_power = 100.0  # MVA base
+        self.inertia_constants = torch.rand(n_buses, device=self.device) * 5.0 + 2.0  # H in seconds
+        
+        # Grid topology - simplified admittance matrix (68x68)
+        self._initialize_grid_topology()
+        
+        # State space: [frequencies (68), generator outputs (20), renewables (14), loads (30), time features (8)] = 140
+        self.state_dim = 140
+        self.obs_dim = 15  # Local observation dimension per agent as specified in proposal
+        
+        # Action space: continuous power changes for each agent
+        self.action_space = spaces.Box(
+            low=-1.0, high=1.0, shape=(self.n_agents,), dtype=np.float32
+        )
+        
+        # Observation space: each agent gets local 15-dim observation
+        self.observation_space = spaces.Box(
+            low=-np.inf, high=np.inf, shape=(self.n_agents, self.obs_dim), dtype=np.float32
+        )
+        
+        # Communication delay buffer (2-second SCADA delay)
+        self.delay_steps = 1  # 1 time step × 2 seconds = 2 second delay
+        self.observation_buffer = []
+        
+        # Renewable forecast parameters
+        self.forecast_horizon = 5  # 5 time steps ahead
+        self.renewable_forecasts = torch.zeros(14, self.forecast_horizon, device=self.device)
+        
+        # Initialize state variables
+        self.reset()
+    
+    def _initialize_grid_topology(self):
+        """Initialize the power grid topology and admittance matrix."""
+        # Simplified 68-bus system admittance matrix
+        # In practice, this would be based on actual grid topology
+        self.admittance_matrix = torch.zeros(self.n_buses, self.n_buses, device=self.device)
+        
+        # Create a connected graph structure
+        for i in range(self.n_buses):
+            # Self admittance (diagonal elements)
+            self.admittance_matrix[i, i] = (torch.rand(1, device=self.device) * 10.0 + 5.0).item()
+            
+            # Connect to nearby buses (simplified ring + radial structure)
+            if i < self.n_buses - 1:
+                admittance_val = (torch.rand(1, device=self.device) * 2.0 + 1.0).item()
+                self.admittance_matrix[i, i+1] = -admittance_val
+                self.admittance_matrix[i+1, i] = -admittance_val
+                self.admittance_matrix[i, i] += admittance_val
+                self.admittance_matrix[i+1, i+1] += admittance_val
+        
+        # Add some additional connections for robustness
+        for _ in range(self.n_buses // 4):
+            i, j = torch.randint(0, self.n_buses, (2,)).tolist()
+            if i != j:
+                admittance_val = (torch.rand(1, device=self.device) * 1.0 + 0.5).item()
+                self.admittance_matrix[i, j] = -admittance_val
+                self.admittance_matrix[j, i] = -admittance_val
+                self.admittance_matrix[i, i] += admittance_val
+                self.admittance_matrix[j, j] += admittance_val
+        
+        # Agent-to-bus mapping (which buses have controllable agents)
+        self.agent_bus_mapping = torch.randint(0, self.n_buses, (self.n_agents,), device=self.device)
+    
+    def reset(self, seed: Optional[int] = None, options: Optional[Dict] = None) -> Tuple[np.ndarray, Dict]:
+        """
+        Reset the environment to initial state.
+        
+        Returns:
+            observations: Array of shape (n_agents, obs_dim)
+            info: Dictionary with additional information
+        """
+        if seed is not None:
+            torch.manual_seed(seed)
+            np.random.seed(seed)
+            random.seed(seed)
+        
+        # Initialize bus frequencies around nominal (60 Hz)
+        self.frequencies = torch.ones(self.n_buses, device=self.device) * self.nominal_frequency
+        self.frequencies += torch.randn(self.n_buses, device=self.device) * 0.01  # Small initial deviation
+        
+        # Initialize generator outputs (MW) within capacity bounds
+        self.generator_outputs = torch.zeros(self.n_agents, device=self.device)
+        # Set initial outputs to minimum capacity for gas plants and mid-range for batteries
+        self.generator_outputs[:5] = 50.0  # Batteries at 50% capacity
+        self.generator_outputs[5:13] = 100.0  # Gas plants at minimum
+        self.generator_outputs[13:] = -50.0  # DR at 25% load reduction
+        
+        # Initialize loads (MW) - distribute total system load across buses
+        # Total system load should be 2000-5000 MW as per proposal
+        load_min, load_max = self.load_range
+        total_system_load = torch.rand(1, device=self.device).item() * (load_max - load_min) + load_min
+        load_distribution = torch.rand(self.n_buses, device=self.device)
+        self.loads = (load_distribution / load_distribution.sum()) * total_system_load
+        
+        # Initialize renewable generation (14 buses with renewables)
+        self.renewable_buses = torch.randint(0, self.n_buses, (14,), device=self.device)
+        self.renewable_generation = torch.rand(14, device=self.device) * 500.0  # 0-500 MW
+        
+        # Initialize voltage angles (radians)
+        self.voltage_angles = torch.zeros(self.n_buses, device=self.device)
+        
+        # Reset contingency state
+        self.contingency_active = False
+        self.contingency_bus = None
+        
+        # Time step counter and time features
+        self.time_step = 0
+        self.current_hour = 12.0  # Start at noon
+        self.current_day = 1.0    # Monday
+        
+        # Initialize observation buffer for communication delay
+        initial_obs = self._get_observations_immediate()
+        self.observation_buffer = [initial_obs.clone() for _ in range(self.delay_steps + 1)]
+        
+        # Initialize renewable forecasts
+        self._update_renewable_forecasts()
+        
+        # Get initial observations (with delay)
+        observations = self._get_observations()
+        info = self._get_info()
+        
+        return observations.cpu().numpy(), info
+    
+    def step(self, actions: np.ndarray) -> Tuple[np.ndarray, float, bool, bool, Dict]:
+        """
+        Execute one time step of the environment.
+        
+        Args:
+            actions: Array of shape (n_agents,) with continuous actions in [-1, 1]
+            
+        Returns:
+            observations: Next state observations
+            reward: Shared reward for all agents
+            terminated: Whether episode is terminated
+            truncated: Whether episode is truncated
+            info: Additional information
+        """
+        actions = torch.tensor(actions, device=self.device, dtype=torch.float32)
+        
+        # Convert normalized actions to actual power changes (MW)
+        power_changes = actions * self.ramp_rates * self.dt  # Scale by ramp rate and time step
+        
+        # Apply action constraints - check capacity limits before updating
+        feasible_changes = torch.zeros_like(power_changes)
+        for i in range(self.n_agents):
+            current_power = self.generator_outputs[i]
+            desired_change = power_changes[i]
+            
+            # Calculate feasible change respecting capacity bounds
+            max_increase = self.power_max[i] - current_power
+            max_decrease = self.power_min[i] - current_power
+            
+            feasible_changes[i] = torch.clamp(desired_change, max_decrease, max_increase)
+        
+        # Apply feasible power changes to generator outputs
+        self.generator_outputs += feasible_changes
+        
+        # Ensure outputs stay within bounds (safety check)
+        self.generator_outputs = torch.clamp(self.generator_outputs, self.power_min, self.power_max)
+        
+        # Store action for wear calculation
+        self.last_actions = feasible_changes
+        
+        # Update time features
+        self._update_time_features()
+        
+        # Update stochastic loads and renewable generation
+        self._update_stochastic_components()
+        
+        # Update renewable forecasts
+        self._update_renewable_forecasts()
+        
+        # Check for N-1 contingencies
+        self._check_contingencies()
+        
+        # Solve power flow and update frequencies using swing equation
+        self._update_system_dynamics()
+        
+        # Calculate reward
+        reward = self._calculate_reward()
+        
+        # Check termination conditions
+        terminated, truncated = self._check_termination()
+        
+        # Update observation buffer and get delayed observations
+        current_obs = self._get_observations_immediate()
+        self.observation_buffer.append(current_obs)
+        if len(self.observation_buffer) > self.delay_steps + 1:
+            self.observation_buffer.pop(0)
+        
+        # Get delayed observations
+        observations = self._get_observations()
+        
+        # Update time step
+        self.time_step += 1
+        
+        info = self._get_info()
+        
+        return observations.cpu().numpy(), reward, terminated, truncated, info
+    
+    def _update_stochastic_components(self):
+        """Update stochastic load and renewable generation."""
+        # Add random variations to loads while maintaining total system load in [2000, 5000] MW
+        # Add ±5% variation to individual bus loads
+        load_variation = torch.randn(self.n_buses, device=self.device) * 0.05
+        self.loads *= (1.0 + load_variation)
+        
+        # Rescale to maintain total system load within bounds
+        current_total = torch.sum(self.loads)
+        load_min, load_max = self.load_range
+        if current_total < load_min:
+            self.loads *= (load_min / current_total)
+        elif current_total > load_max:
+            self.loads *= (load_max / current_total)
+        
+        # Ensure no negative loads
+        self.loads = torch.clamp(self.loads, min=0.0)
+        
+        # Update renewable generation with stochastic variations
+        renewable_variation = torch.randn(14, device=self.device) * 0.1
+        self.renewable_generation *= (1.0 + renewable_variation)
+        self.renewable_generation = torch.clamp(self.renewable_generation, 0.0, 1000.0)
+    
+    def _check_contingencies(self):
+        """Check for N-1 contingency events."""
+        if torch.rand(1).item() < self.contingency_prob:
+            if not self.contingency_active:
+                # Activate contingency - disconnect a random bus
+                self.contingency_active = True
+                self.contingency_bus = torch.randint(0, self.n_buses, (1,)).item()
+                # Reduce load at contingency bus
+                self.loads[self.contingency_bus] *= 0.1
+        else:
+            # Recover from contingency
+            if self.contingency_active:
+                self.contingency_active = False
+                if self.contingency_bus is not None:
+                    # Restore load
+                    load_min, load_max = self.load_range
+                    self.loads[self.contingency_bus] = torch.rand(1, device=self.device) * (load_max - load_min) + load_min
+                self.contingency_bus = None
+    
+    def _update_system_dynamics(self):
+        """Update system dynamics using swing equation."""
+        # Calculate power imbalance at each bus
+        power_injection = torch.zeros(self.n_buses, device=self.device)
+        
+        # Add generator outputs to their respective buses
+        for i, bus_idx in enumerate(self.agent_bus_mapping):
+            power_injection[bus_idx] += self.generator_outputs[i]
+        
+        # Add renewable generation
+        for i, bus_idx in enumerate(self.renewable_buses):
+            power_injection[bus_idx] += self.renewable_generation[i]
+        
+        # Subtract loads
+        power_injection -= self.loads
+        
+        # Swing equation from proposal: df/dt = (P_gen - P_load - P_losses) / (2 * H * S_base)
+        # where S_base is the base power (MVA), not nominal frequency
+        
+        # Calculate electrical power flow (simplified)
+        frequency_deviations = self.frequencies - self.nominal_frequency
+        electrical_power = torch.matmul(self.admittance_matrix, frequency_deviations)
+        
+        # Power imbalance
+        power_imbalance = power_injection - electrical_power
+        
+        # Update frequencies using correct swing equation (use base_power, not nominal_frequency)
+        frequency_derivative = power_imbalance / (2.0 * self.inertia_constants * self.base_power)
+        self.frequencies += frequency_derivative * self.dt * 60.0  # Convert minutes to seconds
+        
+        # NO CLAMPING - let physics run its course for realistic dynamics
+    
+    def _calculate_reward(self):
+        """
+        Calculate the shared reward with graduated response.
+        Uses exponential penalties to create soft boundaries before hard violations.
+        """
+        frequency_deviations = self.frequencies - self.nominal_frequency
+        
+        # 1. Base frequency penalty from proposal: -1000 * sum of squared deviations
+        frequency_penalty = 1000.0 * torch.sum(frequency_deviations ** 2)
+        
+        # 2. Exponential penalty as frequencies approach operational bounds [59.5, 60.5]
+        # Creates a "soft boundary" that strongly discourages approaching limits
+        operational_margin = 0.5  # Hz (operational bounds from proposal)
+        beyond_operational = torch.abs(frequency_deviations) - operational_margin
+        beyond_operational = torch.clamp(beyond_operational, min=0.0)  # Only penalize if beyond
+        exponential_penalty = 5000.0 * torch.sum(torch.exp(5.0 * beyond_operational) - 1.0)
+        
+        # 3. Agent-specific costs: C_i per MW adjusted (from proposal equation 2)
+        if hasattr(self, 'last_actions'):
+            agent_costs = torch.sum(self.cost_coefficients * torch.abs(self.last_actions))
+        else:
+            agent_costs = 0.0
+        
+        # 4. Wear-and-tear functions: 0.1 * W_i(|ΔP^i|) (from proposal equation 2)
+        # Note: Using quadratic wear W_i * (ΔP^i)^2 for smoother gradients
+        if hasattr(self, 'last_actions'):
+            wear_costs = 0.1 * torch.sum(self.wear_coefficients * (self.last_actions ** 2))
+        else:
+            wear_costs = 0.0
+        
+        # 5. Hard safety constraint violations: 10,000 per bus violating operational bounds
+        freq_violations = torch.sum((torch.abs(frequency_deviations) > operational_margin))
+        safety_violations = freq_violations * 10000.0
+        
+        # Total reward (negative because we minimize costs)
+        reward = -(frequency_penalty + exponential_penalty + agent_costs + wear_costs + safety_violations)
+        
+        return reward.item()
+    
+    def _get_observations(self):
+        """Get delayed observations for each agent (2-second SCADA delay)."""
+        if len(self.observation_buffer) >= self.delay_steps + 1:
+            return self.observation_buffer[-(self.delay_steps + 1)]  # Return delayed observation
+        else:
+            return self.observation_buffer[0]  # Return most recent if buffer not full
+    
+    def _get_observations_immediate(self):
+        """Get immediate (non-delayed) observations for each agent."""
+        observations = torch.zeros(self.n_agents, self.obs_dim, device=self.device)
+        
+        # Calculate system frequency deviation (key coordination signal)
+        system_freq_deviation = torch.mean(self.frequencies - self.nominal_frequency)
+        
+        for i in range(self.n_agents):
+            bus_idx = self.agent_bus_mapping[i]
+            obs_idx = 0
+            
+            # Local bus frequency (1)
+            observations[i, obs_idx] = self.frequencies[bus_idx]
+            obs_idx += 1
+            
+            # Local bus load (1)
+            observations[i, obs_idx] = self.loads[bus_idx]
+            obs_idx += 1
+            
+            # Own generator output (1)
+            observations[i, obs_idx] = self.generator_outputs[i]
+            obs_idx += 1
+            
+            # System frequency deviation Δf_sys = (1/68)Σ(f_k - 60) (1)
+            observations[i, obs_idx] = system_freq_deviation
+            obs_idx += 1
+            
+            # Nearby bus frequencies (5 nearest buses)
+            distances = torch.abs(torch.arange(self.n_buses, device=self.device) - bus_idx)
+            _, nearest_indices = torch.topk(distances, k=6, largest=False)  # 6 to exclude self
+            nearest_indices = nearest_indices[1:]  # Remove self
+            observations[i, obs_idx:obs_idx+5] = self.frequencies[nearest_indices]
+            obs_idx += 5
+            
+            # Renewable generation forecasts - next 3 time steps (3)
+            # Use actual forecast values for next 3 time steps, averaged across all renewable sources
+            if self.renewable_forecasts.shape[1] >= 3:
+                # Average forecast across all renewable sources for next 3 time steps
+                observations[i, obs_idx:obs_idx+3] = torch.mean(self.renewable_forecasts[:, :3], dim=0)
+            else:
+                # Fallback if not enough forecast steps
+                observations[i, obs_idx:obs_idx+3] = torch.mean(self.renewable_generation)
+            obs_idx += 3
+            
+            # Time features: hour of day, day of week (2)
+            observations[i, obs_idx] = self.current_hour / 24.0  # Normalized hour
+            observations[i, obs_idx+1] = self.current_day / 7.0  # Normalized day
+            
+            # Total: 1+1+1+1+5+3+2 = 14, need 1 more for 15
+            # Add own capacity utilization (1)
+            capacity_range = self.power_max[i] - self.power_min[i]
+            if capacity_range > 0:
+                utilization = (self.generator_outputs[i] - self.power_min[i]) / capacity_range
+            else:
+                utilization = 0.0
+            observations[i, 14] = utilization
+        
+        return observations
+    
+    def _check_termination(self):
+        """
+        Check if episode should be terminated or truncated.
+        Uses graduated termination criteria - only catastrophic failures terminate.
+        """
+        # Critical violations: ±1.0 Hz from nominal (proposal bounds)
+        critical_violations = torch.sum((self.frequencies < 59.0) | (self.frequencies > 61.0))
+        
+        # Catastrophic violations: ±1.5 Hz from nominal (blackout conditions)
+        catastrophic_violations = torch.sum((self.frequencies < 58.5) | (self.frequencies > 61.5))
+        
+        # Terminate only on:
+        # 1. Catastrophic frequency deviations (±1.5 Hz)
+        # 2. OR >10% of buses in critical state (±1.0 Hz)
+        terminated = (catastrophic_violations > 0) or (critical_violations > 0.1 * self.n_buses)
+        
+        # Truncate after maximum time steps (1000 steps with dt=2s ≈ 33 minutes)
+        truncated = self.time_step >= 1000
+        
+        return terminated, truncated
+    
+    def _update_time_features(self):
+        """Update time-based features (hour of day, day of week)."""
+        # Advance time by dt minutes
+        self.current_hour += self.dt / 60.0
+        if self.current_hour >= 24.0:
+            self.current_hour -= 24.0
+            self.current_day += 1.0
+            if self.current_day > 7.0:
+                self.current_day = 1.0
+    
+    def _update_renewable_forecasts(self):
+        """Update renewable generation forecasts for next 5-15 minutes."""
+        # Simple forecast model: current + trend + noise
+        for i in range(14):
+            current_gen = self.renewable_generation[i]
+            
+            # Add trend (seasonal pattern)
+            hour_tensor = torch.tensor(self.current_hour, device=self.device)
+            trend = 10.0 * torch.sin(2 * math.pi * hour_tensor / 24.0)  # Daily pattern
+            
+            # Add noise
+            noise = torch.randn(self.forecast_horizon, device=self.device) * 20.0
+            
+            # Generate forecast
+            for t in range(self.forecast_horizon):
+                forecast = current_gen + trend * (t + 1) + noise[t]
+                self.renewable_forecasts[i, t] = torch.clamp(forecast, 0.0, 1000.0)
+    
+    def get_full_state(self):
+        """Get the complete 140-dimensional state vector for centralized critic."""
+        # State components: frequencies (68) + generator outputs (20) + renewables (14) + loads (30) + time features (8) = 140
+        state = torch.zeros(self.state_dim, device=self.device)
+        
+        idx = 0
+        # Bus frequencies (68)
+        state[idx:idx+68] = self.frequencies
+        idx += 68
+        
+        # Generator outputs (20)
+        state[idx:idx+20] = self.generator_outputs
+        idx += 20
+        
+        # Renewable generation (14)
+        state[idx:idx+14] = self.renewable_generation
+        idx += 14
+        
+        # Loads (first 30 buses - most critical/largest loads)
+        state[idx:idx+30] = self.loads[:30]
+        idx += 30
+        
+        # Time features (8): hour, day, hour_sin, hour_cos, day_sin, day_cos, load_pattern, renewable_pattern
+        state[idx] = self.current_hour / 24.0  # Normalized hour
+        state[idx+1] = self.current_day / 7.0  # Normalized day
+        
+        # Convert to tensors for torch trig functions
+        hour_tensor = torch.tensor(self.current_hour, device=self.device)
+        day_tensor = torch.tensor(self.current_day, device=self.device)
+        
+        state[idx+2] = torch.sin(2 * math.pi * hour_tensor / 24.0)  # Hour sine
+        state[idx+3] = torch.cos(2 * math.pi * hour_tensor / 24.0)  # Hour cosine
+        state[idx+4] = torch.sin(2 * math.pi * day_tensor / 7.0)   # Day sine
+        state[idx+5] = torch.cos(2 * math.pi * day_tensor / 7.0)   # Day cosine
+        state[idx+6] = torch.mean(self.loads)  # Average load pattern
+        state[idx+7] = torch.mean(self.renewable_generation)  # Average renewable pattern
+        
+        return state
+    
+    def _get_info(self):
+        """Get additional information about the environment state."""
+        system_freq_deviation = torch.mean(self.frequencies - self.nominal_frequency).item()
+        
+        return {
+            'time_step': self.time_step,
+            'mean_frequency': torch.mean(self.frequencies).item(),
+            'frequency_std': torch.std(self.frequencies).item(),
+            'system_freq_deviation': system_freq_deviation,
+            'total_generation': torch.sum(self.generator_outputs).item(),
+            'total_load': torch.sum(self.loads).item(),
+            'contingency_active': self.contingency_active,
+            'safety_violations': torch.sum((self.frequencies < self.frequency_bounds[0]) | 
+                                         (self.frequencies > self.frequency_bounds[1])).item(),
+            'current_hour': self.current_hour,
+            'current_day': self.current_day,
+            'agent_capacity_utilization': [(self.generator_outputs[i] - self.power_min[i]) / 
+                                         (self.power_max[i] - self.power_min[i]) 
+                                         for i in range(self.n_agents)]
+        }
+    
+    def render(self, mode='human'):
+        """Render the environment (optional)."""
+        if mode == 'human':
+            print(f"Step: {self.time_step}")
+            print(f"Mean frequency: {torch.mean(self.frequencies):.3f} Hz")
+            print(f"Frequency range: [{torch.min(self.frequencies):.3f}, {torch.max(self.frequencies):.3f}] Hz")
+            print(f"Total generation: {torch.sum(self.generator_outputs):.1f} MW")
+            print(f"Total load: {torch.sum(self.loads):.1f} MW")
+            print(f"Contingency active: {self.contingency_active}")
+            print("-" * 50)
+    
+    def close(self):
+        """Clean up resources."""
+        pass
diff --git a/readme.md b/readme.md
index 1524cc5..e69de29 100644
--- a/readme.md
+++ b/readme.md
@@ -1,129 +0,0 @@
-# Course Project LaTeX Template
-
-A minimal, opinionated template for course-project writeups. Works on Overleaf and locally.
-
----
-
-## Quick Start
-
-1. **Copy the template** (clone or download).
-2. **Edit metadata** in `main.tex` (title, authors, date).
-3. **Write content** by adding `.tex` files under `sections/` and `\input{...}` them from `main.tex`.
-4. **Add figures** to the `figure/` folder and include them with `\includegraphics`.
-5. **Manage references** in `refs.bib` and cite with `\cite{...}`.
-6. **Compile** on Overleaf or locally (see below).
-
----
-
-## Compile Options
-
-- **Online:** Overleaf — upload the repo and set `main.tex` as the root document.
-- **Local:** VS Code with LaTeX or any editor.
-
----
-
-## Project Structure
-
-```
-.
-├── main.tex                 # Entry point: metadata, packages, inputs, bibliography
-├── preamble_packages.tex    # Package imports (comment out what you don't need)
-├── preamble_symbols.tex     # Common symbols and math operators
-├── shortcuts.tex            # Project-specific commands/macros
-├── refs.bib                 # BibTeX database
-├── sections/                # Source for individual sections
-│   ├── intro.tex
-│   ├── related_work.tex
-│   └── ...
-├── figure/                  # Images and plots
-│   ├── system_diagram.pdf
-│   └── ...
-└── .gitignore               # Files to exclude from version control
-```
-
-
----
-
-## How to Use Each File
-
-- **`main.tex`**
-  - Sets the document class, title, authors, packages, and bibliography.
-  - Includes content, e.g.:
-    ```tex
-    \input{preamble_packages}
-    \input{preamble_symbols}
-    \input{shortcuts}
-
-    \title{Project Title}
-    \author{Alice Smith \and Bob Jones}
-    \date{\today}
-
-    \begin{document}
-    \maketitle
-
-    \input{sections/intro}
-    \input{sections/related_work}
-    \input{sections/method}
-    \input{sections/experiments}
-    \input{sections/conclusion}
-
-    \bibliographystyle{abbrvnat}
-    \bibliography{refs}
-    \end{document}
-    ```
-
-- **`preamble_packages.tex`**
-  - Curated package list. Comment out lines you don’t need to keep the build lean.
-
-- **`preamble_symbols.tex`**
-  - Common math symbols/operators (e.g., `\R`, `\E`, `\argmin`). Extend as needed.
-
-- **`shortcuts.tex`**
-  - Project-specific macros:
-    ```tex
-    \newcommand{\method}{\textsc{OurMethod}\xspace}
-    ```
-
-- **`refs.bib`**
-  - Add BibTeX entries and cite them:
-    ```tex
-    As shown by \cite{mnih2015dqn}, ...
-    ```
-
-- **`sections/`**
-  - Split the paper into maintainable pieces:
-    - `intro.tex`, `related_work.tex`, `method.tex`, `experiments.tex`, `conclusion.tex`, etc.
-  - Include with `\input{sections/<name>}` (no `.tex` extension required).
-
-- **`figure/`**
-  - Store figures/plots and include them:
-    ```tex
-    \begin{figure}[t]
-      \centering
-      \includegraphics[width=\linewidth]{figure/system_diagram}
-      \caption{System overview.}
-      \label{fig:system}
-    \end{figure}
-    ```
-
-- **`.gitignore`**
-  - Keeps build artifacts and OS/editor files out of Git (e.g., `*.aux`, `*.log`, `*.out`, `*.synctex.gz`).
-
----
-
-## Best Practices
-
-- **Labels & refs:** `\label{sec:method}` then `\S\ref{sec:method}`; figures with `\ref{fig:system}`; equations with `\eqref{eq:loss}`.
-- **Tables/Figures:** Prefer vector formats (`.pdf`, `.eps`) for diagrams; use high-resolution `.png` for raster images.
-- **Keep it lean:** Only load packages you need; define macros once in `shortcuts.tex`.
-
----
-
-## Troubleshooting
-
-- **Missing references/citations:** Run LaTeX → BibTeX → LaTeX → LaTeX, or just use `latexmk -pdf`.
-- **Undefined control sequence:** A macro may live in `shortcuts.tex` or a package is missing—ensure it’s included.
-
----
-
-Happy writing!