From fd7335b21657f8438617b116a32dc6d17897081d Mon Sep 17 00:00:00 2001
From: alolika bhowmik <152315710+alo7lika@users.noreply.github.com>
Date: Thu, 31 Oct 2024 01:21:47 +0530
Subject: [PATCH] Create Program.c

---
 .../Program.c                                 | 81 +++++++++++++++++++
 1 file changed, 81 insertions(+)
 create mode 100644 Machine_Learning_Algorithms/Proximal Policy Optimization (PPO)Algorithm /Program.c

diff --git a/Machine_Learning_Algorithms/Proximal Policy Optimization (PPO)Algorithm /Program.c b/Machine_Learning_Algorithms/Proximal Policy Optimization (PPO)Algorithm /Program.c
new file mode 100644
index 00000000..be24f25e
--- /dev/null
+++ b/Machine_Learning_Algorithms/Proximal Policy Optimization (PPO)Algorithm /Program.c	
@@ -0,0 +1,81 @@
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+
+#define TRAJECTORY_LENGTH 100
+#define NUM_TRAJECTORIES 10
+#define CLIP_EPSILON 0.2
+#define LEARNING_RATE 0.001
+#define GAMMA 0.99
+#define LAMBDA 0.95
+
+// Placeholder functions for the neural network
+double policy(double state) {
+    // Simple placeholder function for policy
+    return state * 0.1;
+}
+
+double value_function(double state) {
+    // Simple placeholder function for value function
+    return state * 0.5;
+}
+
+// Calculate advantage function using Generalized Advantage Estimation (GAE)
+double calculate_advantage(double rewards[], double values[], int t) {
+    double advantage = 0.0;
+    double discount = 1.0;
+    for (int k = t; k < TRAJECTORY_LENGTH; ++k) {
+        advantage += discount * (rewards[k] + GAMMA * values[k + 1] - values[k]);
+        discount *= GAMMA * LAMBDA;
+    }
+    return advantage;
+}
+
+// Policy update with clipping
+double clipped_objective(double ratio, double advantage) {
+    double clip_value = fmax(1 - CLIP_EPSILON, fmin(1 + CLIP_EPSILON, ratio));
+    return fmin(ratio * advantage, clip_value * advantage);
+}
+
+// Main PPO loop
+void PPO() {
+    double states[TRAJECTORY_LENGTH];
+    double actions[TRAJECTORY_LENGTH];
+    double rewards[TRAJECTORY_LENGTH];
+    double values[TRAJECTORY_LENGTH];
+    double advantages[TRAJECTORY_LENGTH];
+    double returns[TRAJECTORY_LENGTH];
+
+    for (int episode = 0; episode < NUM_TRAJECTORIES; ++episode) {
+        // Simulate data collection
+        for (int t = 0; t < TRAJECTORY_LENGTH; ++t) {
+            states[t] = (double)t;             // Placeholder state
+            actions[t] = policy(states[t]);    // Take action according to policy
+            rewards[t] = -fabs(actions[t]);    // Placeholder reward function
+            values[t] = value_function(states[t]);
+        }
+
+        // Calculate returns and advantages
+        for (int t = 0; t < TRAJECTORY_LENGTH; ++t) {
+            returns[t] = rewards[t] + GAMMA * values[t + 1];
+            advantages[t] = calculate_advantage(rewards, values, t);
+        }
+
+        // Update policy using clipped objective
+        for (int t = 0; t < TRAJECTORY_LENGTH; ++t) {
+            double old_policy = policy(states[t]);
+            double ratio = policy(states[t]) / old_policy;  // Placeholder policy ratio
+            double objective = clipped_objective(ratio, advantages[t]);
+            
+            // Simple gradient update (mock update, as no neural network here)
+            // In practice, we would use neural network gradients
+            double policy_update = LEARNING_RATE * objective;
+            printf("Policy updated for state %f with value %f\n", states[t], policy_update);
+        }
+    }
+}
+
+int main() {
+    PPO();
+    return 0;
+}