advanced rl techniques
# Advanced RL Techniques
## Introduction & Motivation
Frontier techniques combining multiple RL paradigms. Integration with world models, curriculum learning, and auxiliary tasks. Tackles long-horizon and sparse reward challenges.
Motivation: Address limitations of single-paradigm approaches.
Applications: Complex environments, real-world control, multi-objective tasks.
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## Core Concepts & Theory
### World Models
Learn environment simulator.
### Auxiliary Tasks
Multi-task learning benefits.
### Curriculum Learning
Progressive task difficulty.
### Intrinsic Motivation
Self-supervised learning signals.
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## Mathematical Formulation
World Model:
$$p(s_{t+1}, r_t | s_t, a_t) = p(s_{t+1} | s_t, a_t) p(r_t | s_t, a_t)$$
Auxiliary Loss:
$$L_{ ext{total}} = L_{ ext{RL}} + \lambda_{ ext{aux}} L_{ ext{auxiliary}}$$
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## Advanced Theory & Extensions
### Dreamer
Learn and control world models.
### Plan2Explore
Exploration using imagined trajectories.
### Curriculum Strategies
Automatic difficulty scheduling.
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## Computational Considerations
World Model: O(D²).
Auxiliary Tasks: O(K·D).
Total: O(D² + K·D).
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## Practical Implementation Strategies
### Loss Balancing
Weight multiple objectives.
### Task Scheduling
Progress through curriculum.
### Imagination Rollouts
Generate synthetic experience.
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## Benchmark Datasets & Evaluation
Atari 100k: Sample efficiency.
Robotic Manipulation: Real-world control.
Vision-Based: Pixel observation spaces.
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## Key Challenges & Limitations
### Optimization Complexity
Multiple objectives to balance.
### Scalability
Expensive auxiliary networks.
### Tuning
Sensitive to hyperparameters.
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## Hyperparameter Tuning
Auxiliary weight: 0.1-1.0.
Curriculum steps: 10-50.
Imagination horizon: 5-15.
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## Real-World Applications & Case Studies
Robotics: Complex manipulation.
Game AI: Long-horizon reasoning.
Autonomous Systems: Multi-objective control.
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## Integration with Other Methods
Advanced RL + representation learning; + model-based planning; + human feedback.
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## Summary & Key Takeaways
Advanced techniques combine multiple RL paradigms for powerful learning.
Principles:
1. Integration: Combine approaches.
2. Auxiliary: Multi-task benefits.
3. Curriculum: Progressive learning.
4. World Model: Environment understanding.
5. Imagination: Synthetic experience.
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## Appendix: Practical Labs
### Lab 1: World Model Learning
import numpy as np
class WorldModel:
def __init__(self, state_dim=10, action_dim=2, latent_dim=8):
self.state_dim = state_dim
self.action_dim = action_dim
self.latent_dim = latent_dim
# Encoder and decoder
self.encoder = np.random.randn(state_dim, latent_dim) * 0.01
self.decoder = np.random.randn(latent_dim, state_dim) * 0.01
self.dynamics = np.random.randn(latent_dim + action_dim, latent_dim) * 0.01
self.reward_predictor = np.random.randn(latent_dim, 1) * 0.01
def encode(self, state):
"""Encode to latent space"""
latent = state @ self.encoder
return latent
def decode(self, latent):
"""Decode from latent space"""
state = latent @ self.decoder
return state
def predict_next(self, latent, action):
"""Predict next latent state"""
la = np.concatenate([latent, action])
next_latent = la @ self.dynamics
return next_latent
def predict_reward(self, latent):
"""Predict reward"""
reward = (latent @ self.reward_predictor)[0]
return reward
def train_step(self, state, action, next_state, reward, lr=0.01):
"""Training step"""
# Encode
latent = self.encode(state)
next_latent_true = self.encode(next_state)
# Predict
next_latent_pred = self.predict_next(latent, action)
reward_pred = self.predict_reward(latent)
# Losses
dynamics_loss = np.linalg.norm(next_latent_pred - next_latent_true)
reward_loss = (reward_pred - reward) ** 2
# Updates (simplified)
self.dynamics += lr * np.random.randn(*self.dynamics.shape) * 0.001
model = WorldModel()
state = np.random.randn(10)
action = np.random.randn(2)
next_state = state + action.mean() * 0.1
reward = 1.0
model.train_step(state, action, next_state, reward)
print(f"✓ World model trained")### Lab 2: Auxiliary Task Learning
import numpy as np
def multi_task_loss(main_loss, auxiliary_losses, auxiliary_weights):
"""Combine main and auxiliary losses"""
total_loss = main_loss
for aux_loss, weight in zip(auxiliary_losses, auxiliary_weights):
total_loss += weight * aux_loss
return total_loss
def auxiliary_tasks(state, action, reward):
"""Compute various auxiliary losses"""
tasks = {
'contrastive': np.random.randn(), # Contrastive learning
'inverse_model': np.random.randn(), # Inverse model loss
'self_supervised': np.random.randn(), # Self-supervised learning
}
return tasks
main_loss = 1.5
aux_tasks = auxiliary_tasks(None, None, None)
aux_losses = list(aux_tasks.values())
aux_weights = [0.1, 0.05, 0.05]
total_loss = multi_task_loss(main_loss, aux_losses, aux_weights)
print(f"✓ Multi-task loss: main={main_loss:.2f}, total={total_loss:.2f}")### Lab 3: Curriculum Learning
import numpy as np
class CurriculumScheduler:
def __init__(self, num_levels=5):
self.num_levels = num_levels
self.current_level = 0
self.step = 0
def get_task_difficulty(self):
"""Get current task difficulty"""
difficulty = self.current_level / self.num_levels
return difficulty
def update(self, performance, threshold=0.8):
"""Update curriculum based on performance"""
self.step += 1
if performance > threshold and self.current_level < self.num_levels - 1:
self.current_level += 1
print(f"✓ Advancing curriculum to level {self.current_level}")
return self.get_task_difficulty()
def sample_task(self):
"""Sample task at current difficulty"""
task_param = np.random.randn() * (self.current_level / self.num_levels)
return task_param
scheduler = CurriculumScheduler(num_levels=5)
for _ in range(10):
performance = np.random.rand() # Simulate performance
difficulty = scheduler.update(performance, threshold=0.6)
task = scheduler.sample_task()
print(f"✓ Curriculum progression: level {scheduler.current_level}")### Lab 4: Advanced RL Pipeline
import numpy as np
class AdvancedRLPipeline:
def __init__(self, state_dim=10, action_dim=2):
self.state_dim = state_dim
self.action_dim = action_dim
# Components
self.policy = np.random.randn(state_dim, action_dim) * 0.01
self.value = np.random.randn(state_dim, 1) * 0.01
self.world_model = np.random.randn(state_dim + action_dim, state_dim) * 0.01
self.curriculum = CurriculumScheduler(num_levels=3)
def compute_auxiliary_task_loss(self, state, next_state, action):
"""Auxiliary task: next state prediction"""
predicted_next = (np.concatenate([state, action]) @ self.world_model)
aux_loss = np.linalg.norm(predicted_next - next_state) ** 2
return aux_loss
def compute_main_loss(self, state, action, reward):
"""Main RL loss"""
action_logits = state @ self.policy
action_loss = -np.log(np.exp(action_logits[int(action*2)]) / np.sum(np.exp(action_logits)))
return action_loss
def training_step(self, state, action, next_state, reward, lr=0.01):
"""Complete training step with curriculum"""
# Get difficulty
difficulty = self.curriculum.get_task_difficulty()
# Compute losses
main_loss = self.compute_main_loss(state, action, reward)
aux_loss = self.compute_auxiliary_task_loss(state, next_state, action)
# Combined loss
total_loss = main_loss + 0.1 * aux_loss
# Update
self.policy += lr * np.random.randn(*self.policy.shape) * 0.001
# Update curriculum
performance = 1.0 / (1.0 + total_loss)
self.curriculum.update(performance, threshold=0.7)
return total_loss
class CurriculumScheduler:
def __init__(self, num_levels=5):
self.num_levels = num_levels
self.current_level = 0
def get_task_difficulty(self):
return self.current_level / self.num_levels
def update(self, performance, threshold=0.8):
if performance > threshold and self.current_level < self.num_levels - 1:
self.current_level += 1
return self.get_task_difficulty()
pipeline = AdvancedRLPipeline()
state = np.random.randn(10)
action = 0
for _ in range(10):
loss = pipeline.training_step(state, 0, state*0.9, 1.0)
print(f"✓ Advanced RL pipeline: final curriculum level={pipeline.curriculum.current_level}")---