Reinforcement Learning
Learning Through Interaction & Rewards
Build adaptive AI systems that learn through trial and error
learner & decision maker
world agent interacts with
possible moves
success signals
RL Algorithms & Methods
Value-Based Methods
Q-Learning
- • Learn Q-values for each action
- • Model-free approach
- • Suitable for discrete action spaces
Deep Q-Network (DQN)
- • Use neural networks to approximate Q-function
- • Handle large state spaces
- • Uses experience replay and target networks
Double DQN & Dueling DQN
- • Reduce Q-learning overestimation
- • Separate state value and action advantage
- • Improve learning stability
Policy-Based Methods
Policy Gradient (REINFORCE)
- • Learn policy directly
- • Handle continuous action spaces
- • Use gradient ascent to maximize expected reward
Actor-Critic Methods
- • Combine policy gradient with value function
- • Reduce learning variance
- • Examples: A3C, A2C, PPO
Proximal Policy Optimization (PPO)
- • Carefully update policy
- • Prevent too large policy changes
- • Popular in current applications
Advanced RL Techniques
Multi-Agent RL
Cooperative Learning
Multiple agents cooperate
Competitive Learning
Agents compete against each other
Communication
Inter-agent communication
Hierarchical RL
Options Framework
High-level actions
Goal-Conditioned RL
Goal-oriented learning
Feudal Networks
Hierarchical structure
Meta-Learning RL
MAML
Fast adaptation learning
Learning to Learn
Learn how to learn
Transfer Learning
Knowledge transfer
Offline RL
Batch RL
Learn from existing data
Conservative Q-Learning
Conservative learning approach
Behavior Cloning
Imitate behavior
Inverse RL
Reward Learning
Learn reward function
GAIL
Adversarial imitation
Preference Learning
Learn from preferences
Safe RL
Constrained RL
Learning under constraints
Risk-Aware RL
Risk-aware learning
Robust RL
Robust to changes
Real-World Applications
Gaming & Entertainment
Game AI
AlphaGo, Dota 2 OpenAI Five, StarCraft II AlphaStar
NPC Behavior
Adaptive and learning character behaviors
Game Balancing
Balance games based on player behavior
Robotics & Autonomous Vehicles
Autonomous Driving
Adaptive driving and navigation behaviors
Robot Manipulation
Robotic arm control and object manipulation
Drone Navigation
Autonomous flight and navigation
💰 Finance
- • Algorithmic Trading
- • Portfolio Management
- • Risk Assessment
🏭 Industry
- • Supply Chain Optimization
- • Energy Management
- • Resource Allocation
🏥 Healthcare
- • Treatment Planning
- • Drug Discovery
- • Personalized Medicine
Implementation Guide
Tools & Libraries
Python Libraries
- • Stable Baselines3: Easy-to-use RL library
- • Ray RLlib: Distributed RL platform
- • OpenAI Gym: Testing environments
Deep Learning Frameworks
- • PyTorch: Flexible and user-friendly
- • TensorFlow: Production-ready
- • JAX: Fast with functional programming
Development Process
Define Problem
Define state, action, reward
Choose Algorithm
Select based on problem characteristics
Build Environment
Simulate environment
Train & Tune
Train and tune hyperparameters
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