01 Oct Reinforcement Learning
In Reinforcement learning, the Machine Learning model learns based on the rewards it received (or penalties) for its previous actions. The level of difficulty increases as you get better in the game. It includes the agent and environment. In Reinforcement learning, an agent learns how to behave in an environment by performing actions and seeing the results.
Components of Reinforcement Learning
- Agent: The learner or decision-maker (like the cat).
- Environment: The world in which the agent interacts (like the park where the cat learns tricks).
- Actions: What the agent can do (e.g., sit, fetch, roll over).
- Rewards: Feedback from the environment (e.g., a treat for sitting).
Process of Reinforcement Learning
- Exploration: The agent tries different actions to see their outcomes.
- Exploitation: The agent uses the knowledge gained to choose actions that maximize the reward.
- Learning: The agent continually updates its strategy based on the feedback.
Over time, the agent learns the optimal path through trial and error, improving its performance.
Example 1: Imagine a robot learning to navigate a maze.
The following are the components:
- Agent: The robot.
- Environment: The maze.
- Actions: Move forward, turn left, turn right, etc.
- Rewards: Positive reward for moving closer to the goal, negative reward for hitting a wall.
The following is the process:
- Exploration: The agent tries different actions to see their outcomes.
- Exploitation: The agent uses the knowledge gained to choose actions that maximize the reward.
- Learning: The agent continually updates its strategy based on the feedback.
Over time, the agent learns the optimal path through trial and error, improving its performance.
Example 2: Imagine teaching a self-driving car to navigate city streets:
The following are the components:
- Agent: The self-driving car.
- Environment: The roads, traffic lights, pedestrians, other vehicles, etc.
- Actions: Accelerate, brake, turn left, turn right, stop.
- Rewards: Positive reward for safely navigating intersections, stopping at traffic lights, and maintaining speed limits. The negative reward for near-collisions, sudden braking, and running red lights.
The following is the process:
- Initial Stage: The car starts with a basic understanding of driving rules but makes plenty of mistakes.
- Exploration: The car tries different actions in various scenarios, like stopping at different distances from a pedestrian crossing or accelerating through a yellow light.
- Exploitation: Over time, it starts to identify patterns. For example, it learns that stopping too far from a stop line might be safe, but it causes traffic issues.
- Learning: With each trip, the car updates its driving strategy based on the rewards and penalties received.
The following is the example use case:
- The car approaches a busy intersection.
- It receives a positive reward for stopping when the light is red.
- It gets a small positive reward for smoothly accelerating when the light turns green.
- It receives a negative reward for braking too hard because it approached the intersection too quickly.
Example 3: Imagine a robot playing a game of chess with Reinforcement learning.
The following are the components:
- Agent: The robot.
- Environment: The chessboard and pieces.
- Actions: Moving pieces according to chess rules.
- Rewards: Positive reward for winning, negative reward for losing, and smaller rewards/penalties for making strategic or poor moves.
The following is the process:
- Initial Stage: The Robot starts with no knowledge of chess strategies. It makes random moves, learning the basic rules as it plays against itself or other players.
- Exploration: It tries different moves, even if they seem odd at first, to understand their outcomes. For example, moving a pawn, a knight, or casting a check.
- Exploitation: Over time, the Robot starts to identify patterns. It remembers which moves lead to better positions or victories and begins to favor these moves. For instance, it might learn that controlling the center of the board is beneficial.
- Learning: With each game, Robot updates its strategy based on the rewards and penalties received. It refines its approach by learning from past mistakes and successes.
Imagine a Robot in a game. The following is the example use case:
- It moves a pawn forward (no immediate reward or penalty).
- It develops its pieces, aiming to control the center (small positive reward).
- It sacrifices a piece for a strategic advantage (reward or penalty depending on the outcome).
- It checkmates the opponent (large positive reward).
Reinforcement Learning Techniques/ Algorithms
The Reinforcement Learning techniques include:
- Q-learning: Q-learning is a foundational algorithm in reinforcement learning. It assists an agent to learn how to act optimally in an environment by maximizing cumulative rewards.
The robot uses Q-learning to learn which actions lead it closer to the goal and adjusts its strategy to maximize its total reward over time. - Deep Q Networks (DQN): Deep Q Networks (DQNs) are a blend of Q-learning and deep learning. Q-Learning as discussed above is itself a popular reinforcement learning technique.
- Policy Gradients: Policy Gradients are a type of reinforcement learning algorithm. It focuses on directly learning the policy—a strategy that the agent uses to determine actions based on states—rather than trying to learn the value of each state or state-action pair like in Q-learning.
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