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Jump into concepts and practice - no test required
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import numpy as np import random from collections import deque class SelfImprovingAgent: def __init__(self, action_space, state_space, learning_rate=0.1, discount_factor=0.95): self.action_space = action_space self.state_space = state_space self.lr = learning_rate self.gamma = discount_factor self.q_table = np.zeros((state_space, action_space)) self.experience_memory = deque(maxlen=10000) def choose_action(self, state, epsilon=0.1): if random.random() < epsilon: return random.randint(0, self.action_space - 1) else: return np.argmax(self.q_table[state]) def learn(self, state, action, reward, next_state): predict = self.q_table[state, action] target = reward + self.gamma * np.max(self.q_table[next_state]) self.q_table[state, action] += self.lr * (target - predict) self.experience_memory.append((state, action, reward, next_state)) def self_improve(self, batch_size=32): if len(self.experience_memory) < batch_size: return batch = random.sample(self.experience_memory, batch_size) for state, action, reward, next_state in batch: predict = self.q_table[state, action] target = reward + self.gamma * np.max(self.q_table[next_state]) self.q_table[state, action] += self.lr * (target - predict) # Simulated environment for demonstration class SimpleEnv: def __init__(self): self.state_space = 10 self.action_space = 2 self.state = 0 def reset(self): self.state = 0 return self.state def step(self, action): # Simple rule: action 1 moves state forward, action 0 stays if action == 1 and self.state < self.state_space - 1: self.state += 1 reward = 10 else: reward = -1 done = self.state == self.state_space - 1 return self.state, reward, done # Training loop agent = SelfImprovingAgent(action_space=2, state_space=10) env = SimpleEnv() episodes = 100 total_rewards = [] for episode in range(episodes): state = env.reset() done = False total_reward = 0 while not done: epsilon = max(0.01, 0.2 * (0.995 ** episode)) action = agent.choose_action(state, epsilon) next_state, reward, done = env.step(action) agent.learn(state, action, reward, next_state) state = next_state total_reward += reward total_rewards.append(total_reward) agent.self_improve(batch_size=32) avg_train_reward = np.mean(total_rewards[-20:]) print(f"Recent average train reward: {avg_train_reward:.2f}") # Multi-episode evaluation num_eval = 50 total_correct = 0 total_steps = 0 eval_rewards = [] for _ in range(num_eval): state = env.reset() done = False corr = 0 steps = 0 ep_r = 0 while not done: action = agent.choose_action(state, epsilon=0) next_state, reward, done = env.step(action) if action == 1: corr += 1 ep_r += reward steps += 1 state = next_state total_correct += corr total_steps += steps eval_rewards.append(ep_r) accuracy = (total_correct / total_steps * 100) if total_steps > 0 else 0 avg_eval_reward = np.mean(eval_rewards) print(f"Agent accuracy: {accuracy:.2f}%") print(f"Average eval reward per episode: {avg_eval_reward:.2f}")
Before: Accuracy 60%, Reward 50 per episode.
After: Accuracy 100%, Reward 78 per episode.
self-improving agent in AI?actions = ['move', 'turn', 'scan']
results = [True, False, True]
agent_knowledge = {'move': 0.5, 'turn': 0.5, 'scan': 0.5}
for i in range(len(actions)):
if results[i]:
agent_knowledge[actions[i]] += 0.1
else:
agent_knowledge[actions[i]] -= 0.1
print(agent_knowledge)
What will be the printed output?actions = ['jump', 'run']
results = [True, False]
knowledge = {'jump': 0.3, 'run': 0.7}
for i in range(len(actions)):
if results[i]:
knowledge[actions[i]] += 0.1
else:
knowledge[actions[i]] =- 0.1
print(knowledge)
What is the bug and how to fix it?