Reinforcement Learning
Reinforcement learning is a machine learning method where an agent learns by interacting with an environment. The agent takes actions, receives rewards, and improves its strategy over time.
Core Idea
The agent learns a policy. The policy tells the agent which action to take in each state. The goal is to maximize long term reward.
How Reinforcement Learning Works
- The agent observes a state.
- The agent picks an action.
- The environment returns a reward and a new state.
- The agent updates its policy based on the reward.
- The cycle repeats until the policy improves.
Main Components
1. Agent
The learner that chooses actions.
2. Environment
The world where the agent acts.
3. State
The current situation.
4. Action
The decision taken by the agent.
5. Reward
The feedback that guides learning.
6. Policy
The rule for selecting actions.
Types of Reinforcement Learning
1. Value Based RL
The agent learns the value of states or state action pairs. It chooses actions with maximum value.
- Example. Q Learning
2. Policy Based RL
The agent learns the policy directly. It adjusts policy parameters to improve reward.
- Example. REINFORCE
3. Actor Critic Methods
These methods combine value learning and policy learning.
- Examples. A2C and PPO
Exploration vs Exploitation
The agent must explore actions to find better rewards. It must also exploit known good actions. RL balances both.
Popular Algorithms
- Q Learning
- Deep Q Network
- PPO
- SAC
- A2C
Common RL Applications
- Robotics control
- Game playing
- Recommendation systems
- Autonomous navigation
Strengths
- Learns through interaction
- Improves with time
- Works in dynamic environments
Limitations
- Slow learning
- Needs many interactions
- Sensitive to reward design
Reinforcement Learning in Moroccan Darija
Reinforcement learning howa tariqa li kayt3llam fiha agent b interaction m3a environment. Agent kaydir action, kayakhod reward, w kayhssen policy.
Kif Kaykhddam
- Agent kaychouf state.
- Kaydir action.
- Environment kayrje3 reward w state jdid.
- Agent kayupdate policy.
Types
- Value based. Q Learning.
- Policy based. REINFORCE.
- Actor critic. PPO.
Applications
- Robots.
- Games.
- Recommendations.
Conclusion
Reinforcement learning builds agents that learn from rewards. It supports control, decision making, and adaptive behavior. It forms a strong branch of machine learning.