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.
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.
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.
Think of exploration as trying new dishes at a restaurant, while exploitation is ordering your favorite meal because you know it's good.
