RL PPO 5×5 מאמן פגיעה במטרה : RB1001-06
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import pygame import random import sys pygame.init() # ========================================================= # BOARD # ========================================================= ROWS = 10 COLS = 5 CELL = 55 WIDTH = COLS * CEL HEIGHT = ROWS * CELL screen = pygame.display.set_mode((WIDTH, HEIGHT)) pygame.display.set_caption("RL PPO Obstacle Avoidance") clock = pygame.time.Clock() font = pygame.font.SysFont("arial", 32, bold=True) small_font = pygame.font.SysFont("arial", 17, bold=True) WHITE = (255, 255, 255) BLACK = (0, 0, 0) LIGHT = (240, 240, 240) HIDDEN = (190, 190, 190) RED = (220, 0, 0) GREEN = (0, 150, 0) # ========================================================= # AGENT # ========================================================= agent_row = ROWS - 1 agent_col = 2 # Agent sees only 6 rows ahead VISION_ROWS = 6 # ========================================================= # OBSTACLES # ========================================================= # Each obstacle = [row, column] obstacles = [] # Normally: # 1-3 empty rows between obstacles rows_until_next_obstacle = random.randint(1, 3) # Every 50 road steps: # create a long empty section of 6 rows LONG_GAP_EVERY = 50 LONG_GAP_SIZE = 6 long_gap_remaining = 0 # ========================================================= # COLUMN DISTRIBUTION # ========================================================= # Count how many obstacles were generated # in each column column_count = [0] * COLS # ========================================================= # STATISTICS # ========================================================= score = 0 last_reward = 0 avoided = 0 collisions = 0 unnecessary_moves = 0 # Number of road rows moved rows_passed = 0 # ========================================================= # RED X COLLISION EFFECT # ========================================================= x_active = False x_row = agent_row x_col = 0 x_until = 0 # ========================================================= # WORLD TIMER # ========================================================= MOVE_EVENT = pygame.USEREVENT + 1 # Road moves every 500 milliseconds pygame.time.set_timer(MOVE_EVENT, 500) # ========================================================= # CHOOSE OBSTACLE COLUMN # ========================================================= def choose_obstacle_column(): """ Choose mainly from columns that have received fewer obstacles, for a more balanced distribution. """ minimum_count = min(column_count) possible_columns = [ col for col in range(COLS) if column_count[col] == minimum_count ] selected_col = random.choice(possible_columns) column_count[selected_col] += 1 return selected_col # ========================================================= # CHECK WHETHER AN OBSTACLE IS IN AGENT VISION # ========================================================= def obstacle_in_vision(): """ Returns True if at least one obstacle exists inside the 6 rows in front of A. """ first_visible_row = max( 0, agent_row - VISION_ROWS ) for row, col in obstacles: if first_visible_row <= row < agent_row: return True return False # ========================================================= # CREATE NEW TOP ROAD ROW # ========================================================= def create_new_top_row(): global rows_until_next_obstacle global long_gap_remaining # ----------------------------------------------------- # Forced 6-row empty section # ----------------------------------------------------- if long_gap_remaining > 0: long_gap_remaining -= 1 return # ----------------------------------------------------- # Normal empty-row spacing # ----------------------------------------------------- if rows_until_next_obstacle > 0: rows_until_next_obstacle -= 1 return # ----------------------------------------------------- # Create exactly one obstacle # ----------------------------------------------------- obstacle_col = choose_obstacle_column() obstacles.append([ 0, obstacle_col ]) # Next obstacle after 1-3 empty rows rows_until_next_obstacle = random.randint( 1, 3 ) # ========================================================= # REGISTER COLLISION # ========================================================= def register_collision(col): global score global last_reward global collisions global x_active global x_row global x_col global x_until score -= 10 last_reward = -10 collisions += 1 # Red X x_active = True x_row = agent_row x_col = col # X remains visible for 500 ms x_until = pygame.time.get_ticks() + 500 # ========================================================= # CHECK COLLISION AFTER AGENT MOVES # ========================================================= def check_agent_collision(): global obstacles for obstacle in obstacles[:]: row, col = obstacle if ( row == agent_row and col == agent_col ): register_collision(col) # Remove obstacle so collision is counted once obstacles.remove(obstacle) return True return False # ========================================================= # MOVE AGENT # ========================================================= def move_agent(direction): global agent_col global score global last_reward global unnecessary_moves # Check whether danger was visible BEFORE moving danger_visible = obstacle_in_vision() old_col = agent_col # LEFT if direction == -1: agent_col -= 1 # RIGHT elif direction == 1: agent_col += 1 # Keep agent inside board agent_col = max( 0, min(COLS - 1, agent_col) ) moved = agent_col != old_col # Tried to move outside the board if not moved: return # Check if A moved directly into O collided = check_agent_collision() if collided: return # ----------------------------------------------------- # PUNISH UNNECESSARY MOVEMENT # ----------------------------------------------------- if not danger_visible: score -= 1 last_reward = -1 unnecessary_moves += 1 # ========================================================= # MOVE WORLD # ========================================================= def move_world(): global obstacles global score global last_reward global avoided global rows_passed global long_gap_remaining global rows_until_next_obstacle # One more road row passed rows_passed += 1 # ----------------------------------------------------- # Every 50 rows create a 6-row empty section # ----------------------------------------------------- if rows_passed % LONG_GAP_EVERY == 0: long_gap_remaining = LONG_GAP_SIZE # Cancel current normal spacing rows_until_next_obstacle = 0 new_obstacles = [] for row, col in obstacles: new_row = row + 1 # ------------------------------------------------- # OBSTACLE REACHES AGENT ROW # ------------------------------------------------- if new_row == agent_row: # Collision if col == agent_col: register_collision(col) # Remove obstacle continue # Successfully avoided else: score += 10 last_reward = +10 avoided += 1 new_obstacles.append([ new_row, col ]) # ------------------------------------------------- # NORMAL MOVEMENT # ------------------------------------------------- elif new_row < ROWS: new_obstacles.append([ new_row, col ]) obstacles = new_obstacles create_new_top_row() # ========================================================= # DRAW GRID # ========================================================= def draw_grid(): screen.fill(WHITE) vision_start = ( agent_row - VISION_ROWS ) for row in range(ROWS): for col in range(COLS): rect = pygame.Rect( col * CELL, row * CELL, CELL, CELL ) # Visible region if row >= vision_start: pygame.draw.rect( screen, LIGHT, rect ) # Hidden region else: pygame.draw.rect( screen, HIDDEN, rect ) pygame.draw.rect( screen, BLACK, rect, 1 ) # ========================================================= # DRAW LETTER # ========================================================= def draw_letter( letter, row, col, color=BLACK ): text = font.render( letter, True, color ) x = col * CELL + CELL // 2 y = row * CELL + CELL // 2 rect = text.get_rect( center=(x, y) ) screen.blit( text, rect ) # ========================================================= # DRAW INFORMATION # ========================================================= def draw_information(): # Score screen.blit( small_font.render( f"Score: {score}", True, BLACK ), (5, 5) ) # Last reward reward_color = BLACK if last_reward > 0: reward_color = GREEN elif last_reward < 0: reward_color = RED screen.blit( small_font.render( f"Reward: {last_reward:+d}", True, reward_color ), (5, 25) ) # Avoided obstacles screen.blit( small_font.render( f"Avoided: {avoided}", True, GREEN ), (5, 45) ) # Collisions screen.blit( small_font.render( f"Collisions: {collisions}", True, RED ), (5, 65) ) # Unnecessary moves screen.blit( small_font.render( f"Bad moves: {unnecessary_moves}", True, RED ), (5, 85) ) # Road rows passed screen.blit( small_font.render( f"Rows: {rows_passed}", True, BLACK ), (5, 105) ) # Obstacle distribution per column screen.blit( small_font.render( f"O: {column_count}", True, BLACK ), (5, 125) ) # ========================================================= # MAIN LOOP # ========================================================= running = True while running: current_time = pygame.time.get_ticks() # ----------------------------------------------------- # Remove red X after 500 ms # Game continues normally # ----------------------------------------------------- if ( x_active and current_time >= x_until ): x_active = False # ----------------------------------------------------- # EVENTS # ----------------------------------------------------- for event in pygame.event.get(): if event.type == pygame.QUIT: running = False # ------------------------------------------------- # KEYBOARD # ------------------------------------------------- elif event.type == pygame.KEYDOWN: if event.key == pygame.K_ESCAPE: running = False elif event.key == pygame.K_LEFT: move_agent(-1) elif event.key == pygame.K_RIGHT: move_agent(+1) # ------------------------------------------------- # MOVE ROAD # ------------------------------------------------- elif event.type == MOVE_EVENT: move_world() # ===================================================== # DRAW # ===================================================== draw_grid() vision_start = ( agent_row - VISION_ROWS ) # Draw only obstacles inside visible range for row, col in obstacles: if row >= vision_start: draw_letter( "O", row, col ) # Draw agent draw_letter( "A", agent_row, agent_col ) # Draw red X after collision if x_active: draw_letter( "X", x_row, x_col, RED ) draw_information() pygame.display.flip() clock.tick(60) pygame.quit() sys.exit() |
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import random import sys from collections import deque from datetime import datetime from pathlib import Path import numpy as np import pygame import torch import torch.nn as nn import torch.optim as optim from torch.distributions import Categorical # ============================================================ # Configuration # ============================================================ ROWS = 10 COLS = 5 VISION_ROWS = 6 CELL_SIZE = 50 TITLE_HEIGHT = 58 GRID_WIDTH = COLS * CELL_SIZE GRID_HEIGHT = ROWS * CELL_SIZE LEFT_PANEL_WIDTH = 420 RIGHT_PANEL_WIDTH = 390 BOARD_X = LEFT_PANEL_WIDTH BOARD_Y = TITLE_HEIGHT + 48 RIGHT_PANEL_X = LEFT_PANEL_WIDTH + GRID_WIDTH WINDOW_WIDTH = LEFT_PANEL_WIDTH + GRID_WIDTH + RIGHT_PANEL_WIDTH WINDOW_HEIGHT = 760 MAX_EPISODE_STEPS = 300 ROLLOUT_STEPS = 64 # Rewards AVOID_REWARD = 10.0 COLLISION_PENALTY = -25.0 UNNECESSARY_MOVE_PENALTY = -0.01 INVALID_MOVE_PENALTY = -0.10 STAY_REWARD = 0.0 # AI input importance by DISPLAYED row number. # Rows 9-10 are the most important, row 8 is medium, # row 7 is much less important, and distant rows are weak hints. # # NOTE: row 10 is the agent row. After a successful avoid, the obstacle # is removed because it has already passed the agent; therefore the main # immediate danger signal normally comes from row 9. ROW_IMPORTANCE = { 4: 0.10, 5: 0.15, 6: 0.20, 7: 0.30, 8: 0.55, 9: 1.00, 10: 1.00, } # Obstacle spacing NORMAL_GAP_MIN = 1 NORMAL_GAP_MAX = 3 # Once every 50 road steps create 6 empty rows LONG_GAP_EVERY = 50 LONG_GAP_SIZE = 6 # Red X duration after collision HIT_EFFECT_MS = 500 # Actual real-time delay between PPO/game decisions. # Larger number = slower program. SPEED_LEVELS_MS = [ 16000, 8000, 4000, 2000, 1000, 500, 250, 125, 63, 31, ] DEFAULT_SPEED_INDEX = SPEED_LEVELS_MS.index(500) ACTION_NAMES = ["STAY", "LEFT", "RIGHT"] ACTION_COUNT = len(ACTION_NAMES) # 6 rows ahead + A row = 7 rows * 5 columns = 35 # + one-hot A position = 5 # Total = 40 inputs OBSERVED_ROWS = VISION_ROWS + 1 OBSERVATION_SIZE = OBSERVED_ROWS * COLS + COLS DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") # ============================================================ # Colors # ============================================================ WHITE = (255, 255, 255) BLACK = (20, 20, 20) LIGHT_GRAY = (238, 238, 238) MID_GRAY = (215, 215, 215) DARK_GRAY = (70, 70, 70) GRID_GRAY = (195, 195, 195) HIDDEN_GRAY = (170, 170, 170) RED = (220, 0, 0) BLUE = (20, 70, 170) GREEN = (0, 135, 55) ORANGE = (205, 105, 0) PURPLE = (115, 45, 150) # ============================================================ # Environment # ============================================================ class ObstacleEnvironment: """10x5 obstacle-avoidance environment used directly by PPO.""" def __init__(self): # Lifetime statistics self.total_rows = 0 self.total_avoided = 0 self.total_collisions = 0 self.total_bad_moves = 0 self.total_invalid_moves = 0 self.score = 0.0 # Keep obstacle distribution balanced across columns self.column_count = [0] * COLS self.episode_number = 0 self.reset() @property def agent_row(self): return ROWS - 1 def reset(self): self.agent_col = random.randrange(COLS) self.obstacles = [] self.rows_until_next_obstacle = random.randint( NORMAL_GAP_MIN, NORMAL_GAP_MAX, ) self.long_gap_remaining = 0 self.next_long_gap_step = LONG_GAP_EVERY self.episode_steps = 0 self.episode_road_steps = 0 self.episode_reward = 0.0 self.last_result = "READY" self.last_reward = 0.0 self.episode_number += 1 return self.observation() def observation(self): """ AI input: 35 values = rows 4-10 (7 rows x 5 columns) 5 values = one-hot agent column Total = 40 inputs. Row 10 (A row) is included, so A can see O beside it. """ visible = np.zeros((OBSERVED_ROWS, COLS), dtype=np.float32) first_visible_row = self.agent_row - VISION_ROWS for row, col in self.obstacles: if first_visible_row <= row <= self.agent_row: visible_row = row - first_visible_row # Convert zero-based environment row to displayed row 1..10. display_row = row + 1 # A nearby obstacle is represented by a larger input value. # This gives PPO a built-in proximity bias: # row 9/10 > row 8 > row 7 > distant rows. visible[visible_row, col] = ROW_IMPORTANCE.get( display_row, 0.10, ) agent_position = np.zeros(COLS, dtype=np.float32) agent_position[self.agent_col] = 1.0 return np.concatenate( [visible.reshape(-1), agent_position] ).astype(np.float32) def danger_immediately_above_agent(self): """True only if O is one row directly above A in the same column.""" danger_row = self.agent_row - 1 for row, col in self.obstacles: if row == danger_row and col == self.agent_col: return True return False def _choose_obstacle_column(self): """ Select among the least-used columns. This keeps O distributed across all 5 columns. """ minimum_count = min(self.column_count) possible_columns = [ col for col in range(COLS) if self.column_count[col] == minimum_count ] selected_col = random.choice(possible_columns) self.column_count[selected_col] += 1 return selected_col def _create_new_top_row(self): """ Normal rule: 1-3 empty rows between obstacles. Special rule: once every 50 road steps, force 6 empty rows. """ # Start a special long gap exactly once at the threshold. if ( self.episode_road_steps >= self.next_long_gap_step and self.long_gap_remaining == 0 ): self.long_gap_remaining = LONG_GAP_SIZE self.rows_until_next_obstacle = 0 self.next_long_gap_step += LONG_GAP_EVERY # Long forced empty section if self.long_gap_remaining > 0: self.long_gap_remaining -= 1 return # Normal empty spacing if self.rows_until_next_obstacle > 0: self.rows_until_next_obstacle -= 1 return # Create exactly ONE obstacle in the new top row. obstacle_col = self._choose_obstacle_column() self.obstacles.append([0, obstacle_col]) # Next normal gap self.rows_until_next_obstacle = random.randint( NORMAL_GAP_MIN, NORMAL_GAP_MAX, ) def _remove_obstacle_at_agent(self): """ Used when A moves LEFT/RIGHT into an O already on A's row. """ for obstacle in self.obstacles[:]: row, col = obstacle if row == self.agent_row and col == self.agent_col: self.obstacles.remove(obstacle) return col return None def step(self, action): reward = STAY_REWARD events = [] collision_col = None # Only O directly above A makes LEFT/RIGHT necessary. danger_before = self.danger_immediately_above_agent() old_agent_col = self.agent_col # ---------------------------------------------------- # Actor action # ---------------------------------------------------- if action == 1: # LEFT if self.agent_col > 0: self.agent_col -= 1 else: reward += INVALID_MOVE_PENALTY self.total_invalid_moves += 1 events.append("INVALID") elif action == 2: # RIGHT if self.agent_col < COLS - 1: self.agent_col += 1 else: reward += INVALID_MOVE_PENALTY self.total_invalid_moves += 1 events.append("INVALID") # action == 0 -> STAY moved = self.agent_col != old_agent_col # ---------------------------------------------------- # A moved directly into an obstacle # ---------------------------------------------------- if moved: immediate_collision = self._remove_obstacle_at_agent() if immediate_collision is not None: reward += COLLISION_PENALTY self.total_collisions += 1 collision_col = immediate_collision events.append("COLLISION") elif not danger_before: # No O directly above A, so moving was unnecessary. reward += UNNECESSARY_MOVE_PENALTY self.total_bad_moves += 1 events.append("BAD_MOVE") # ---------------------------------------------------- # Road moves one row # ---------------------------------------------------- self.total_rows += 1 self.episode_road_steps += 1 new_obstacles = [] for row, col in self.obstacles: new_row = row + 1 # O reaches A row. if new_row == self.agent_row: if col == self.agent_col: # Collision even if A stayed still. reward += COLLISION_PENALTY self.total_collisions += 1 collision_col = col events.append("COLLISION") # Remove this O after collision. continue # A avoided O successfully. reward += AVOID_REWARD self.total_avoided += 1 events.append("AVOID") # The obstacle has passed the agent. Remove it now. # Keeping it on row 10 would allow the next LEFT/RIGHT # decision to collide with an obstacle that was already avoided. continue elif new_row < ROWS: new_obstacles.append([new_row, col]) self.obstacles = new_obstacles # Build next road row. self._create_new_top_row() # ---------------------------------------------------- # Statistics # ---------------------------------------------------- self.score += reward self.last_reward = reward if "COLLISION" in events: self.last_result = "COLLISION" elif "AVOID" in events: self.last_result = "AVOID" elif "BAD_MOVE" in events: self.last_result = "BAD MOVE" elif "INVALID" in events: self.last_result = "INVALID" else: self.last_result = ACTION_NAMES[action] self.episode_steps += 1 self.episode_reward += reward done = self.episode_steps >= MAX_EPISODE_STEPS info = { "events": events, "collision_col": collision_col, "episode_reward": self.episode_reward, } return self.observation(), reward, done, info # ============================================================ # Actor / Critic # ============================================================ class ActorCritic(nn.Module): """Separate PPO actor and critic networks.""" def __init__(self): super().__init__() self.actor = nn.Sequential( nn.Linear(OBSERVATION_SIZE, 64), nn.Tanh(), nn.Linear(64, 64), nn.Tanh(), nn.Linear(64, ACTION_COUNT), ) self.critic = nn.Sequential( nn.Linear(OBSERVATION_SIZE, 64), nn.Tanh(), nn.Linear(64, 64), nn.Tanh(), nn.Linear(64, 1), ) def forward(self, observations): logits = self.actor(observations) values = self.critic(observations).squeeze(-1) return logits, values # ============================================================ # PPO Trainer # ============================================================ class PPOTrainer: def __init__(self): self.model = ActorCritic().to(DEVICE) # Same optimizer style as the uploaded PPO example. self.optimizer = optim.Adam(self.model.parameters(), lr=5e-4) self.gamma = 0.99 self.gae_lambda = 0.95 self.clip_range = 0.20 self.entropy_coefficient = 0.002 self.value_coefficient = 0.50 self.epochs = 8 self.batch_size = 32 self.clear_buffer() self.total_steps = 0 self.update_number = 0 self.actor_loss = 0.0 self.critic_loss = 0.0 self.entropy = 0.0 self.current_value = 0.0 self.current_probabilities = np.full( ACTION_COUNT, 1.0 / ACTION_COUNT, ) self.current_action = 0 self.exploration_steps = 0 self.exploitation_steps = 0 def clear_buffer(self): self.observations = [] self.actions = [] self.log_probabilities = [] self.rewards = [] self.dones = [] self.values = [] def select_action(self, observation): observation_tensor = torch.as_tensor( observation, dtype=torch.float32, device=DEVICE, ).unsqueeze(0) with torch.no_grad(): logits, value = self.model(observation_tensor) distribution = Categorical(logits=logits) action = distribution.sample() log_probability = distribution.log_prob(action) probabilities = distribution.probs.squeeze(0) action_number = int(action.item()) greedy_action = int(torch.argmax(probabilities).item()) explored = action_number != greedy_action if explored: self.exploration_steps += 1 else: self.exploitation_steps += 1 self.current_action = action_number self.current_value = float(value.item()) self.current_probabilities = probabilities.cpu().numpy() self.total_steps += 1 return ( action_number, float(log_probability.item()), float(value.item()), explored, ) def store( self, observation, action, log_probability, reward, done, value, ): self.observations.append(observation.copy()) self.actions.append(action) self.log_probabilities.append(log_probability) self.rewards.append(reward) self.dones.append(done) self.values.append(value) def ready(self): return len(self.observations) >= ROLLOUT_STEPS def update(self, last_observation, last_done): with torch.no_grad(): last_tensor = torch.as_tensor( last_observation, dtype=torch.float32, device=DEVICE, ).unsqueeze(0) _, last_value_tensor = self.model(last_tensor) last_value = ( 0.0 if last_done else float(last_value_tensor.item()) ) rewards = np.asarray(self.rewards, dtype=np.float32) dones = np.asarray(self.dones, dtype=np.float32) values = np.asarray(self.values, dtype=np.float32) advantages = np.zeros_like(rewards) gae = 0.0 for index in reversed(range(len(rewards))): next_value = ( last_value if index == len(rewards) - 1 else values[index + 1] ) next_non_terminal = 1.0 - dones[index] delta = ( rewards[index] + self.gamma * next_value * next_non_terminal - values[index] ) gae = ( delta + self.gamma * self.gae_lambda * next_non_terminal * gae ) advantages[index] = gae returns = advantages + values observations_tensor = torch.as_tensor( np.asarray(self.observations), dtype=torch.float32, device=DEVICE, ) actions_tensor = torch.as_tensor( self.actions, dtype=torch.long, device=DEVICE, ) old_log_probabilities_tensor = torch.as_tensor( self.log_probabilities, dtype=torch.float32, device=DEVICE, ) advantages_tensor = torch.as_tensor( advantages, dtype=torch.float32, device=DEVICE, ) returns_tensor = torch.as_tensor( returns, dtype=torch.float32, device=DEVICE, ) advantages_tensor = ( advantages_tensor - advantages_tensor.mean() ) / (advantages_tensor.std() + 1e-8) sample_count = observations_tensor.shape[0] final_actor_loss = 0.0 final_critic_loss = 0.0 final_entropy = 0.0 for _ in range(self.epochs): indices = torch.randperm( sample_count, device=DEVICE, ) for start in range( 0, sample_count, self.batch_size, ): batch_indices = indices[ start : start + self.batch_size ] logits, predicted_values = self.model( observations_tensor[batch_indices] ) distribution = Categorical(logits=logits) new_log_probabilities = distribution.log_prob( actions_tensor[batch_indices] ) entropy = distribution.entropy().mean() probability_ratio = torch.exp( new_log_probabilities - old_log_probabilities_tensor[batch_indices] ) unclipped = ( probability_ratio * advantages_tensor[batch_indices] ) clipped = ( torch.clamp( probability_ratio, 1.0 - self.clip_range, 1.0 + self.clip_range, ) * advantages_tensor[batch_indices] ) actor_loss = -torch.min( unclipped, clipped, ).mean() critic_loss = nn.functional.mse_loss( predicted_values, returns_tensor[batch_indices], ) total_loss = ( actor_loss + self.value_coefficient * critic_loss - self.entropy_coefficient * entropy ) self.optimizer.zero_grad() total_loss.backward() nn.utils.clip_grad_norm_( self.model.parameters(), 0.5, ) self.optimizer.step() final_actor_loss = float(actor_loss.item()) final_critic_loss = float(critic_loss.item()) final_entropy = float(entropy.item()) self.actor_loss = final_actor_loss self.critic_loss = final_critic_loss self.entropy = final_entropy self.update_number += 1 self.clear_buffer() def save_model(self, environment): script_directory = Path(__file__).resolve().parent save_directory = script_directory / "saved_models" save_directory.mkdir(parents=True, exist_ok=True) timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") model_name = f"ppo_10x5_obstacle_{timestamp}.pt" model_path = (save_directory / model_name).resolve() checkpoint = { "model_state_dict": self.model.state_dict(), "optimizer_state_dict": self.optimizer.state_dict(), "total_steps": self.total_steps, "update_number": self.update_number, "exploration_steps": self.exploration_steps, "exploitation_steps": self.exploitation_steps, "observation_size": OBSERVATION_SIZE, "action_count": ACTION_COUNT, "action_names": ACTION_NAMES, "avoid_reward": AVOID_REWARD, "collision_penalty": COLLISION_PENALTY, "unnecessary_move_penalty": UNNECESSARY_MOVE_PENALTY, "invalid_move_penalty": INVALID_MOVE_PENALTY, "row_importance": ROW_IMPORTANCE, "rows": environment.total_rows, "avoided": environment.total_avoided, "collisions": environment.total_collisions, "bad_moves": environment.total_bad_moves, "column_count": environment.column_count, } torch.save(checkpoint, model_path) return model_name, model_path # ============================================================ # Drawing helpers # ============================================================ def draw_text(surface, font, text, x, y, color=BLACK): surface.blit( font.render(text, True, color), (x, y), ) def draw_character( surface, font, character, row, col, color=BLACK, ): if not ( 0 <= col < COLS and 0 <= row < ROWS ): return center_x = ( BOARD_X + col * CELL_SIZE + CELL_SIZE // 2 ) center_y = ( BOARD_Y + row * CELL_SIZE + CELL_SIZE // 2 ) text = font.render( character, True, color, ) surface.blit( text, text.get_rect( center=(center_x, center_y) ), ) def percentage(part, whole): return ( 0.0 if whole == 0 else 100.0 * part / whole ) def draw_learning_graph( screen, font, small_font, results, ): graph = pygame.Rect( 28, TITLE_HEIGHT + 58, LEFT_PANEL_WIDTH - 56, 330, ) pygame.draw.rect(screen, WHITE, graph) pygame.draw.rect(screen, BLACK, graph, 2) draw_text( screen, font, "REAL-TIME PPO REWARD GRAPH", 28, TITLE_HEIGHT + 20, BLUE, ) inner_left = graph.x + 48 inner_right = graph.right - 12 inner_top = graph.y + 20 inner_bottom = graph.bottom - 34 inner_width = inner_right - inner_left inner_height = inner_bottom - inner_top minimum_reward = -27.0 maximum_reward = 12.0 def reward_to_y(value): value = max( minimum_reward, min(maximum_reward, value), ) return int( inner_top + ( (maximum_reward - value) / (maximum_reward - minimum_reward) ) * inner_height ) graph_levels = [ (10.0, "+10 AVOID", GREEN), (0.0, "0", BLACK), (UNNECESSARY_MOVE_PENALTY, f"{UNNECESSARY_MOVE_PENALTY:g} MOVE", ORANGE), (-25.0, "-25 COLL", RED), ] for value, label, color in graph_levels: y = reward_to_y(value) pygame.draw.line( screen, GRID_GRAY, (inner_left, y), (inner_right, y), 1, ) draw_text( screen, small_font, label, graph.x + 2, y - 8, color, ) if not results: draw_text( screen, small_font, "Waiting for first PPO step...", graph.x + 86, reward_to_y(0.0) - 10, DARK_GRAY, ) else: values = list(results) count = len(values) slot_count = ( results.maxlen or max(2, count) ) first_slot = max( 0, slot_count - count, ) points = [] for index, result in enumerate(values): slot = first_slot + index x = int( inner_left + ( slot / max(1, slot_count - 1) ) * inner_width ) y = reward_to_y(result) points.append((x, y)) if len(points) >= 2: pygame.draw.lines( screen, DARK_GRAY, False, points, 2, ) for (x, y), result in zip(points, values): if result >= 9.0: color = GREEN radius = 5 elif result <= -9.0: color = RED radius = 5 elif result < 0.0: color = ORANGE radius = 4 else: color = BLUE radius = 3 pygame.draw.circle( screen, color, (x, y), radius, ) draw_text( screen, small_font, "Old", inner_left, graph.bottom - 24, DARK_GRAY, ) draw_text( screen, small_font, "Newest", inner_right - 50, graph.bottom - 24, DARK_GRAY, ) draw_text( screen, small_font, "Green +10 avoid", graph.x + 22, graph.bottom + 7, GREEN, ) draw_text( screen, small_font, "Blue 0 neutral", graph.x + 145, graph.bottom + 7, BLUE, ) draw_text( screen, small_font, "Red -25 collision", graph.x + 268, graph.bottom + 7, RED, ) def is_plus_key(event): typed = getattr(event, "unicode", "") return ( typed in ("+", "=") or event.key in ( pygame.K_KP_PLUS, pygame.K_EQUALS, ) ) def is_minus_key(event): typed = getattr(event, "unicode", "") return ( typed in ("-", "_") or event.key in ( pygame.K_MINUS, pygame.K_KP_MINUS, ) ) # ============================================================ # Main program # ============================================================ def main(): random.seed(7) np.random.seed(7) torch.manual_seed(7) pygame.init() pygame.key.set_repeat(350, 120) screen = pygame.display.set_mode( (WINDOW_WIDTH, WINDOW_HEIGHT) ) pygame.display.set_caption( "10x5 RL PPO A.I. TRAINING OBSTACLE AVOIDANCE" ) clock = pygame.time.Clock() title_font = pygame.font.SysFont( "arial", 24, bold=True, ) character_font = pygame.font.SysFont( "arial", 31, bold=True, ) section_font = pygame.font.SysFont( "arial", 19, bold=True, ) text_font = pygame.font.SysFont( "consolas", 16, ) small_font = pygame.font.SysFont( "consolas", 14, ) environment = ObstacleEnvironment() trainer = PPOTrainer() observation = environment.observation() completed_episode_rewards = deque(maxlen=30) graph_history = deque(maxlen=80) collision_effects = [] current_episode_reward = 0.0 latest_step_reward = 0.0 completed_episodes = 0 paused = False running = True last_mode = "EXPLORATION" speed_index = DEFAULT_SPEED_INDEX last_step_time = pygame.time.get_ticks() speed_message = ( f"START: {SPEED_LEVELS_MS[speed_index]} ms/step" ) speed_message_end = last_step_time + 2500 save_message = "Q: save PPO model" save_message_end = 0 minus_button = pygame.Rect( 28, 595, 170, 40, ) plus_button = pygame.Rect( 222, 595, 170, 40, ) while running: # ---------------------------------------------------- # Events # ---------------------------------------------------- for event in pygame.event.get(): if event.type == pygame.QUIT: running = False elif event.type == pygame.KEYDOWN: if event.key == pygame.K_ESCAPE: running = False elif event.key == pygame.K_SPACE: paused = not paused last_step_time = pygame.time.get_ticks() elif event.key == pygame.K_r: environment = ObstacleEnvironment() trainer = PPOTrainer() observation = environment.observation() completed_episode_rewards.clear() graph_history.clear() collision_effects.clear() current_episode_reward = 0.0 latest_step_reward = 0.0 completed_episodes = 0 last_step_time = pygame.time.get_ticks() elif event.key == pygame.K_q: try: model_name, model_path = trainer.save_model( environment ) print( "\nPPO MODEL SAVED", flush=True, ) print( f"Model name: {model_name}", flush=True, ) print( f"Full path : {model_path}", flush=True, ) save_message = f"SAVED: {model_name}" except ( OSError, RuntimeError, ) as error: print( f"\nERROR: could not save PPO model: {error}", flush=True, ) save_message = ( "SAVE ERROR - see terminal" ) save_message_end = ( pygame.time.get_ticks() + 5000 ) elif ( is_plus_key(event) or event.key == pygame.K_e ): old_index = speed_index speed_index = min( speed_index + 1, len(SPEED_LEVELS_MS) - 1, ) speed_message = ( f"FASTER: {SPEED_LEVELS_MS[speed_index]} ms/step" if speed_index != old_index else "MAXIMUM SPEED" ) speed_message_end = ( pygame.time.get_ticks() + 1800 ) elif ( is_minus_key(event) or event.key == pygame.K_w ): old_index = speed_index speed_index = max( speed_index - 1, 0, ) speed_message = ( f"SLOWER: {SPEED_LEVELS_MS[speed_index]} ms/step" if speed_index != old_index else "MINIMUM SPEED" ) speed_message_end = ( pygame.time.get_ticks() + 1800 ) elif ( event.type == pygame.MOUSEBUTTONDOWN and event.button == 1 ): old_index = speed_index if minus_button.collidepoint(event.pos): speed_index = max( speed_index - 1, 0, ) speed_message = ( f"SLOWER: {SPEED_LEVELS_MS[speed_index]} ms/step" if speed_index != old_index else "MINIMUM SPEED" ) speed_message_end = ( pygame.time.get_ticks() + 1800 ) elif plus_button.collidepoint(event.pos): speed_index = min( speed_index + 1, len(SPEED_LEVELS_MS) - 1, ) speed_message = ( f"FASTER: {SPEED_LEVELS_MS[speed_index]} ms/step" if speed_index != old_index else "MAXIMUM SPEED" ) speed_message_end = ( pygame.time.get_ticks() + 1800 ) current_time = pygame.time.get_ticks() step_delay_ms = SPEED_LEVELS_MS[speed_index] # ---------------------------------------------------- # One PPO step # ---------------------------------------------------- if ( not paused and current_time - last_step_time >= step_delay_ms ): last_step_time = current_time old_observation = observation.copy() ( action, log_probability, value, explored, ) = trainer.select_action( old_observation ) last_mode = ( "EXPLORATION" if explored else "EXPLOITATION" ) ( next_observation, reward, done, info, ) = environment.step(action) current_episode_reward += reward latest_step_reward = float(reward) trainer.store( old_observation, action, log_probability, reward, done, value, ) graph_history.append( latest_step_reward ) if ( "COLLISION" in info["events"] and info["collision_col"] is not None ): collision_effects.append( { "col": int( info["collision_col"] ), "start_time": current_time, } ) observation = next_observation if trainer.ready(): trainer.update( observation, done, ) if done: completed_episode_rewards.append( current_episode_reward ) completed_episodes += 1 current_episode_reward = 0.0 observation = environment.reset() # ---------------------------------------------------- # Remove completed X effects # ---------------------------------------------------- collision_effects = [ effect for effect in collision_effects if ( current_time - effect["start_time"] < HIT_EFFECT_MS ) ] # ---------------------------------------------------- # Draw background and title # ---------------------------------------------------- screen.fill(LIGHT_GRAY) pygame.draw.rect( screen, BLACK, ( 0, 0, WINDOW_WIDTH, TITLE_HEIGHT, ), ) draw_text( screen, title_font, "10x5 RL PPO A.I. TRAINING OBSTACLE AVOIDANCE", 14, 14, WHITE, ) # ---------------------------------------------------- # Left panel # ---------------------------------------------------- pygame.draw.rect( screen, MID_GRAY, ( 0, TITLE_HEIGHT, LEFT_PANEL_WIDTH, WINDOW_HEIGHT - TITLE_HEIGHT, ), ) pygame.draw.line( screen, BLACK, ( LEFT_PANEL_WIDTH, TITLE_HEIGHT, ), ( LEFT_PANEL_WIDTH, WINDOW_HEIGHT, ), 3, ) draw_learning_graph( screen, section_font, small_font, graph_history, ) speed_y = 490 draw_text( screen, section_font, "REAL SPEED CONTROL", 28, speed_y, BLUE, ) speed_y += 30 draw_text( screen, text_font, f"AI / road step : {step_delay_ms} ms", 28, speed_y, ) speed_y += 23 draw_text( screen, text_font, f"Actual rate : {1000.0 / step_delay_ms:.3f} steps/s", 28, speed_y, ) speed_y += 23 draw_text( screen, text_font, f"AI input : {OBSERVATION_SIZE} values", 28, speed_y, ) speed_y += 23 draw_text( screen, text_font, "Actions : STAY LEFT RIGHT", 28, speed_y, ) pygame.draw.rect( screen, WHITE, minus_button, ) pygame.draw.rect( screen, RED, minus_button, 3, ) pygame.draw.rect( screen, WHITE, plus_button, ) pygame.draw.rect( screen, GREEN, plus_button, 3, ) minus_label = section_font.render( "- SLOWER", True, RED, ) plus_label = section_font.render( "+ FASTER", True, GREEN, ) screen.blit( minus_label, minus_label.get_rect( center=minus_button.center ), ) screen.blit( plus_label, plus_label.get_rect( center=plus_button.center ), ) progress_rect = pygame.Rect( 28, 650, LEFT_PANEL_WIDTH - 56, 14, ) pygame.draw.rect( screen, WHITE, progress_rect, ) pygame.draw.rect( screen, BLACK, progress_rect, 1, ) elapsed = ( 0 if paused else max( 0, current_time - last_step_time, ) ) progress = min( 1.0, elapsed / step_delay_ms, ) fill_rect = pygame.Rect( progress_rect.x, progress_rect.y, int( progress_rect.width * progress ), progress_rect.height, ) pygame.draw.rect( screen, BLUE, fill_rect, ) draw_text( screen, small_font, ( "Paused" if paused else "Time to next AI step" ), 28, progress_rect.bottom + 5, DARK_GRAY, ) if current_time < speed_message_end: draw_text( screen, small_font, speed_message, 190, progress_rect.bottom + 5, BLUE, ) if current_time < save_message_end: draw_text( screen, small_font, save_message, 28, progress_rect.bottom + 23, GREEN, ) # ---------------------------------------------------- # Center board # ---------------------------------------------------- draw_text( screen, section_font, "10x5 WORLD - INPUT ROWS 4-10", BOARD_X + 12, TITLE_HEIGHT + 14, BLUE, ) vision_start = ( environment.agent_row - VISION_ROWS ) for row in range(ROWS): for col in range(COLS): rectangle = pygame.Rect( BOARD_X + col * CELL_SIZE, BOARD_Y + row * CELL_SIZE, CELL_SIZE, CELL_SIZE, ) if row < vision_start: pygame.draw.rect( screen, HIDDEN_GRAY, rectangle, ) else: pygame.draw.rect( screen, WHITE, rectangle, ) pygame.draw.rect( screen, BLACK, rectangle, 1, ) # Draw O inside AI input region, including A row. for row, col in environment.obstacles: if ( vision_start <= row <= environment.agent_row ): draw_character( screen, character_font, "O", row, col, BLUE, ) draw_character( screen, character_font, "A", environment.agent_row, environment.agent_col, BLACK, ) # Red X for 500 ms after collision. for effect in collision_effects: draw_character( screen, character_font, "X", environment.agent_row, effect["col"], RED, ) controls_y = ( BOARD_Y + GRID_HEIGHT + 16 ) draw_text( screen, small_font, "SPACE: pause/resume", BOARD_X + 42, controls_y, DARK_GRAY, ) draw_text( screen, small_font, "Q: save model", BOARD_X + 75, controls_y + 22, BLUE, ) draw_text( screen, small_font, "R: restart ESC: exit", BOARD_X + 42, controls_y + 44, DARK_GRAY, ) if paused: overlay = pygame.Surface( ( GRID_WIDTH, GRID_HEIGHT, ), pygame.SRCALPHA, ) overlay.fill( ( 255, 255, 255, 195, ) ) screen.blit( overlay, ( BOARD_X, BOARD_Y, ), ) pause_text = title_font.render( "PAUSED", True, RED, ) screen.blit( pause_text, pause_text.get_rect( center=( BOARD_X + GRID_WIDTH // 2, BOARD_Y + GRID_HEIGHT // 2, ) ), ) # ---------------------------------------------------- # Right panel # ---------------------------------------------------- pygame.draw.rect( screen, MID_GRAY, ( RIGHT_PANEL_X, TITLE_HEIGHT, RIGHT_PANEL_WIDTH, WINDOW_HEIGHT - TITLE_HEIGHT, ), ) pygame.draw.line( screen, BLACK, ( RIGHT_PANEL_X, TITLE_HEIGHT, ), ( RIGHT_PANEL_X, WINDOW_HEIGHT, ), 3, ) x = RIGHT_PANEL_X + 16 y = TITLE_HEIGHT + 10 draw_text( screen, section_font, "PPO LEARNING", x, y, BLUE, ) y += 27 draw_text( screen, text_font, f"Device : {str(DEVICE).upper()}", x, y, ) y += 21 draw_text( screen, text_font, f"Learning step: {trainer.total_steps}", x, y, ) y += 21 draw_text( screen, text_font, f"PPO updates : {trainer.update_number}", x, y, ) y += 21 draw_text( screen, text_font, f"Episodes : {completed_episodes}", x, y, ) y += 21 average_reward = ( sum(completed_episode_rewards) / len(completed_episode_rewards) if completed_episode_rewards else 0.0 ) draw_text( screen, text_font, f"Avg episode : {average_reward:8.3f}", x, y, ) y += 21 draw_text( screen, text_font, f"Actor loss : {trainer.actor_loss:8.4f}", x, y, ) y += 21 draw_text( screen, text_font, f"Critic loss : {trainer.critic_loss:8.4f}", x, y, ) y += 21 draw_text( screen, text_font, f"Critic value : {trainer.current_value:8.3f}", x, y, ) y += 29 draw_text( screen, section_font, "ACTOR ACTION", x, y, GREEN, ) y += 25 draw_text( screen, text_font, f"Selected : {ACTION_NAMES[trainer.current_action]}", x, y, ) y += 21 for index, action_name in enumerate( ACTION_NAMES ): probability_value = ( trainer.current_probabilities[index] * 100.0 ) draw_text( screen, small_font, f"P({action_name:<5}) = {probability_value:5.1f}%", x, y, ) y += 18 # Strategy block strategy_total = ( trainer.exploration_steps + trainer.exploitation_steps ) exploration_percent = percentage( trainer.exploration_steps, strategy_total, ) exploitation_percent = percentage( trainer.exploitation_steps, strategy_total, ) mode_color = ( ORANGE if last_mode == "EXPLORATION" else GREEN ) strategy_x = x + 205 draw_text( screen, section_font, "STRATEGY", strategy_x, TITLE_HEIGHT + 10, BLUE, ) strategy_y = TITLE_HEIGHT + 37 draw_text( screen, text_font, last_mode, strategy_x, strategy_y, mode_color, ) strategy_y += 25 draw_text( screen, small_font, f"Explore: {exploration_percent:5.1f}%", strategy_x, strategy_y, ) strategy_y += 19 draw_text( screen, small_font, f"Exploit: {exploitation_percent:5.1f}%", strategy_x, strategy_y, ) # ---------------------------------------------------- # Obstacle statistics # ---------------------------------------------------- table_y = TITLE_HEIGHT + 300 draw_text( screen, section_font, "OBSTACLE TABLE", x, table_y, PURPLE, ) table_y += 27 total_results = ( environment.total_avoided + environment.total_collisions ) avoid_percent = percentage( environment.total_avoided, total_results, ) collision_percent = percentage( environment.total_collisions, total_results, ) obstacle_lines = [ f"Score : {environment.score:8.1f}", f"Rows : {environment.total_rows}", f"Avoided : {environment.total_avoided}", f"Collisions : {environment.total_collisions}", f"Bad moves : {environment.total_bad_moves}", f"Invalid : {environment.total_invalid_moves}", f"Avoid : {avoid_percent:5.1f}%", f"Collision : {collision_percent:5.1f}%", f"Last : {environment.last_result}", f"O columns : {environment.column_count}", ] for line in obstacle_lines: draw_text( screen, small_font, line, x, table_y, ) table_y += 18 # ---------------------------------------------------- # Reward table # ---------------------------------------------------- reward_y = table_y + 10 draw_text( screen, section_font, "REWARDS", x, reward_y, BLUE, ) reward_y += 25 reward_lines = [ ( f"Latest step : {latest_step_reward:+7.3f}", BLACK, ), ( f"Avoid O : {AVOID_REWARD:+7.3f}", GREEN, ), ( f"Collision : {COLLISION_PENALTY:+7.3f}", RED, ), ( f"Bad move : {UNNECESSARY_MOVE_PENALTY:+7.3f}", ORANGE, ), ( f"Invalid move: {INVALID_MOVE_PENALTY:+7.3f}", RED, ), ( f"Stay : {STAY_REWARD:+7.3f}", BLACK, ), ] for line, color in reward_lines: draw_text( screen, small_font, line, x, reward_y, color, ) reward_y += 18 pygame.display.flip() clock.tick(60) pygame.quit() sys.exit() if __name__ == "__main__": main() |
