Gym Environment Components and API Methods

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Questions and Answers

What is the purpose of the observation space in a Gym environment?

To determine how the environment changes with actions.
To specify the possible set of actions.
To outline the format of observations. (correct)
To define the criteria for reward allocation.

Which method in the Gym API is used to initialize the environment?

close()
step()
render()
reset() (correct)

What metric is defined as the sum of rewards over an episode?

Episode Length
Action Quality
Total Reward (correct)
Stability

Which tool is best suited for visualizing performance graphs in Gym?

<p>TensorBoard (C)</p> Signup and view all the answers

What does the step(action) method do in the Gym API?

<p>It applies an action and returns the result. (B)</p> Signup and view all the answers

Flashcards

Observation Space

A way of describing the data the agent observes during the environment's state, like the position of a robot or the current score in a game.

Action Space

A set of all possible actions an agent can take within the environment, like moving left, right, jumping, or shooting.

Reward Structure

A system that determines how the agent receives points or rewards for performing actions in the environment.

State Transition

How the environment changes based on the actions taken by the agent, like moving to the next level after completing a task or changing the game state based on a player's decision.

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Total Reward

A metric that tracks how much reward an agent accumulates over a complete run or episode.

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Study Notes

Gym Environment Components

Observation Space: Defines the format of observations.
Action Space: Defines the set of possible actions.
Reward Structure: Defines how rewards are given.
State Transition: Defines how the environment changes with actions.

Gym API Methods

reset(): Initializes the environment.
step(action): Applies an action and returns the result.
render(): Visualizes the environment.
close(): Cleans up resources.

Evaluating Agent Performance

Metrics:
- Total Reward: Sum of rewards over an episode.
- Episode Length: Number of steps before termination.
- Stability and Consistency: How performance varies across episodes.