What is the difference between value and reward?
As nouns the difference between value and reward is that value is the quality (positive or negative) that renders something desirable or valuable while reward is something of value given in return for an act.
Why choose a high value of the discount factor gamma in Q learning?
gamma is the discount factor. It quantifies how much importance we give for future rewards. It’s also handy to approximate the noise in future rewards.
Why is the discount factor important in reinforcement learning?
Longer time horizons have have much more variance as they include more irrelevant information, while short time horizons are biased towards only short-term gains. The discount factor essentially determines how much the reinforcement learning agents cares about rewards in the distant future relative to those in the immediate future.
What happens when the discount factor γ is 0?
If γ = 0, the agent will be completely myopic and only learn about actions that produce an immediate reward. If γ = 1, the agent will evaluate each of its actions based on the sum total of all of its future rewards. So why wouldn’t you always want to make γ as high as possible? Well, most actions don’t have long-lasting repercussions.
How is the discount factor related to the time horizon?
The value τ explicitly shows the time horizon associated with a discount factor; γ = 1 corresponds to τ = ∞, and any rewards that are much more than τ time steps in the future are exponentially suppressed.
How is the reward used in reinforcement learning?
The reward is the feedback the agent gets from the environment at every time step. It can then use this reward signal (can be positive for a good action or negative for a bad action) to conclude how to behave in a state. The goal, in general, is to solve a given task with the maximum reward possible.