Readme and comments and reward pdated

readme was outdated
env rewards got a penalty for standing still while it should move instead of 0 reward

ml/env.py

@@ -1,5 +1,10 @@
 """
-ml/env.py - Gymnasium Environment for JackBot Hexapod RL Training
+ml/env.py - Gymnasium environment for JackBot RL training and evaluation.
+
+This file defines JackBotEnv, the main training/evaluation environment used by PPO.
+It wraps the PyBullet simulation and Robot interfaces into a Gymnasium-compatible
+step/reset loop, manages command sampling, curriculum progression, and reward
+calculation, and exposes metrics that the training callbacks can log.
 """
 import time
 import math
@@ -363,7 +368,10 @@ class JackBotEnv(gym.Env):
             target_speed = math.hypot(target_vx, target_vy)
 
             if not is_moving:
-                total_reward = 0.0
+                # If the command says move but the robot stays effectively still,
+                # give a real penalty instead of a neutral reward.
+                stillness_penalty = -0.10
+                total_reward = stillness_penalty
             else:
                 lin_vel_error = (filtered_vx - target_vx)**2 + (filtered_vy - target_vy)**2
                 r_lin_vel = math.exp(-25.0 * lin_vel_error)
@@ -371,7 +379,7 @@ class JackBotEnv(gym.Env):
 
                 if target_speed > 0.08 and raw_speed < 0.03:
                     r_lin_vel = 0.0
-                    stillness_penalty = -0.1  # Softened from -0.25
+                    stillness_penalty = -0.05
 
                 w_lin_vel, w_ang_vel, w_height, w_stability, w_smoothness = 0.55, 0.15, 0.10, 0.12, 0.08
                 total_reward = (
@@ -381,7 +389,7 @@ class JackBotEnv(gym.Env):
 
         step_reward = float(total_reward / 10.0)
         alive_bonus = 0.01
-        final_reward = max(0.0, step_reward + jitter_penalty + alive_bonus)
+        final_reward = step_reward + jitter_penalty + alive_bonus
 
         self.last_reward_components = {
             "height": float(r_height),