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Build intelligent agent. Reinforcement learning, autonomy. Project 2024.
Project Overview
Build an AI agent that can autonomously perceive its environment, reason about goals, plan action sequences, and execute them to achieve objectives. This project integrates core AI concepts—search algorithms, knowledge representation, planning, and learning—into a unified system that demonstrates intelligent autonomous behavior.
Unlike simple chatbots that respond reactively, an AI agent maintains internal state, pursues goals proactively, and adapts its strategy based on environmental feedback. This project teaches you to build the complete sense-plan-act loop that underlies all autonomous AI systems.
Architecture Design
| │ │ PERCEPTS │── | │ WORLD MODEL │ │ |
| │ │ GOALS │── | │ PLANNER │ │ |
| │ │ EXECUTOR │── | Actions |
Implementation: Task-Planning Agent
Environment: Grid World with Tasks
Example
class Environment:
def __init__(self, size=10):
self.grid = np.zeros((size, size))
self.objects = {} # {name: (x, y, properties)}
self.agent_pos = (0, 0)
def place_objects(self):
self.objects = {
'key': (3, 7, {'type': 'tool', 'unlocks': 'door'}),
'door': (5, 5, {'type': 'barrier', 'locked': True}),
'goal': (8, 8, {'type': 'objective'}),
'food': (2, 3, {'type': 'resource', 'energy': 20}),
}
def get_percepts(self, pos, vision_range=2):
# Agent can only see nearby objects
visible = {}
for name, (x, y, props) in self.objects.items():
if abs(x-pos[0]) <= vision_range and abs(y-pos[1]) <= vision_range:
visible[name] = (x, y, props)
return visible
Agent with Belief-Desire-Intention (BDI)
Example
class BDIAgent:
def __init__(self):
self.beliefs = {} # What agent knows about world
self.desires = [] # Goals agent wants to achieve
self.intentions = [] # Current plan of action
self.inventory = [] # Objects agent carries
def perceive(self, percepts):
# Update beliefs based on new percepts
for name, (x, y, props) in percepts.items():
self.beliefs[name] = {'pos': (x, y), **props}
def deliberate(self):
# Choose which desire to pursue
if 'goal' in self.beliefs and not self.beliefs.get('door', {}).get('locked'):
return ('reach', 'goal')
elif 'door' in self.beliefs and self.beliefs['door'].get('locked'):
if 'key' not in self.inventory:
return ('get', 'key')
else:
return ('use', 'key', 'door')
else:
return ('explore',)
def plan(self, intention):
# Convert intention to action sequence
if intention[0] == 'reach':
target = self.beliefs[intention[1]]['pos']
return self.path_to(target)
elif intention[0] == 'get':
target = self.beliefs[intention[1]]['pos']
return self.path_to(target) + ['pickup']
elif intention[0] == 'explore':
return self.exploration_strategy()
Planning with A* Search
Example
def path_to(self, target):
# A* pathfinding
start = self.position
open_set = [(heuristic(start, target), start)]
came_from = {}
g_score = {start: 0}
while open_set:
_, current = heapq.heappop(open_set)
if current == target:
return reconstruct_path(came_from, current)
for neighbor in get_neighbors(current):
if is_blocked(neighbor):
continue
tentative_g = g_score[current] + 1
if tentative_g < g_score.get(neighbor, float('inf')):
came_from[neighbor] = current
g_score[neighbor] = tentative_g
f = tentative_g + heuristic(neighbor, target)
heapq.heappush(open_set, (f, neighbor))
return None # No path found
Advanced Features
Learning from Experience
Example
class LearningAgent(BDIAgent):
def __init__(self):
super().__init__()
self.q_table = {} # State-action values
self.experience = [] # Past episodes
def learn_from_episode(self, episode):
# Update Q-values from experience
for state, action, reward, next_state in reversed(episode):
best_next = max(self.q_table.get(next_state, {}).values(), default=0)
current = self.q_table.setdefault(state, {}).get(action, 0)
self.q_table[state][action] = current + 0.1 * (
reward + 0.9 * best_next - current
)
Natural Language Interface
python example
def parse_command(self, text):
# Simple NLU for agent commands
if 'go to' in text:
target = extract_location(text)
self.desires.append(('reach', target))
elif 'pick up' in text:
obj = extract_object(text)
self.desires.append(('get', obj))
elif 'what do you see' in text:
return self.describe_percepts()Project Extensions
| Level 1 (Basic) | Grid navigation with A* pathfinding |
| Level 2 (Intermediate): Multi-step planning (key | door→goal) |
| Level 3 (Advanced) | Learning from experience (Q-learning) |
| Level 4 (Expert) | Multi-agent coordination |
| Level 5 (Research) | LLM-powered reasoning and planning |
Technologies Used
| Core | Python, NumPy |
| Search | A*, BFS for pathfinding |
| Planning | STRIPS-style action representation |
| Learning | Q-learning for policy improvement |
| Optional | PyGame for visualization |
| Optional | OpenAI API for LLM-based reasoning |
Key Learning Outcomes
Example
1. Agent architectures (reactive, deliberative, hybrid)
2. Belief-Desire-Intention (BDI) model
3. Search algorithms applied to planning
4. Knowledge representation for agent beliefs
5. Reinforcement learning for strategy improvement
6. Integration of multiple AI techniques into one system
Interview Questions
Q: What makes an agent intelligent vs. merely reactive? A: An intelligent agent maintains internal state (beliefs about the world), reasons about goals (deliberation), forms plans (sequences of actions to achieve goals), and adapts based on outcomes (learning). A reactive agent simply maps current percepts to actions with no internal reasoning. Intelligence emerges from the integration of perception, reasoning, planning, and learning.
Q: How would you scale this to a real-world agent? A: Replace the grid with real sensor inputs (camera, LiDAR). Use deep learning for perception (object detection, scene understanding). Use LLMs for high-level reasoning and plan generation. Use reinforcement learning for low-level control policies. Add safety constraints and uncertainty handling for real-world robustness.
Continue the lesson path
Study next in this module
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