How an Agent Learns to Make Decisions by Interacting with Its Environment in Reinforcement Learning
The Agent-Environment Interaction
- Agent: The entity that makes decisions.
- Environment: The context in which the agent operates.
- States: The current situation or configuration of the environment.
- Actions: The choices available to the agent.
- Rewards: Feedback from the environment evaluating the agent's actions.
- Policies: Strategies that map states to actions.
Note
The state space is the set of all possible states, while the action space is the set of all possible actions.
Cumulative Reward
- Definition: The total reward an agent accumulates over time.
- Goal: Maximize this reward by making optimal decisions.
- Discount Factor: A value (usually between 0 and 1) that prioritizes immediate rewards over distant ones.
Example
In a game, collecting coins might provide immediate rewards, while completing a level offers a larger, long-term reward.
Exploration vs. Exploitation
- Exploration: Trying new actions to discover their potential rewards.
- Exploitation: Choosing actions that are known to yield high rewards.
Analogy
Think of exploration as trying new dishes at a restaurant, while exploitation is ordering your favorite meal because you know it's good.
Balancing Exploration and Exploitation
- Epsilon-Greedy Strategy: The agent exploits the best-known action most of the time but explores random actions with a small probability (epsilon).
- Upper Confidence Bound (UCB): Chooses actions that maximize an upper confidence limit of the expected reward.
- Thompson Sampling: Maintains a probability distribution of how good each action might be and samples from it to make decisions.
Tip
Finding the right balance between exploration and exploitation is crucial for maximizing long-term rewards.
A Game-Playing Agent Example
- Environment: A platform game with terrains, obstacles, and rewards.
- Agent: A character controlled by AI.
- States: Character's position, nearby obstacles, and collectibles.
- Actions: Move left, move right, jump, etc.
- Rewards: Positive for collecting items, negative for losing health.
Example
As the agent plays, it learns which actions lead to higher scores, gradually improving its strategy.
Learning and Policy Update
- Feedback Loop: The agent updates its strategy based on the rewards received.
- Techniques: Methods like Q-learning or Deep Q-Networks (DQN) are used to estimate the expected future rewards for each action.
Note
An agent might need hundreds of thousands of iterations to master a complex game level.
