What is difference between reward & discount factor?

Discount factor is a value between 0 and 1. A reward R that occurs N steps in the future from the current state, is multiplied by γ^N to describe its importance to the current state. For example consider γ = 0.9 and a reward R = 10 that is 3 steps ahead of our current state.

What is the difference between a small gamma discount factor and a large gamma?

The larger the gamma, the smaller the discount (so We get decent future rewards). This means the agent focuses more about the long term reward. On the other hand, the smaller the gamma, the bigger the discount(so We get terrible future rewards).

Which of the following are differences between episodic tasks and continuing tasks?

A continuous task can go on forever, an episodic task has at least one finite state (i.e. an end of the game). Mathematically speaking an episodic task has a state with transition probability 1 to itself and 0 anywhere else.

What is episodic in Reinforcement Learning?

In episodic reinforcement learning (RL), an agent interacts with an environment in episodes of length H. The quality of a RL algorithm, which adaptively selects the next action to perform based on past observation, can be measured with different performance metrics.

Why do we use a discount factor gamma 1 for continuing tasks?

What is the role of the discount factor in RL? The discount factor, 𝛾, is a real value ∈ [0, 1], cares for the rewards agent achieved in the past, present, and future. In different words, it relates the rewards to the time domain. If 𝛾 = 1, the agent cares for all future rewards.

Why do we need a discount factor in reinforcement learning?

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. If γ=0, the agent will be completely myopic and only learn about actions that produce an immediate reward.

What is episodic task?

Episodic tasks are the tasks that have a terminal state (end). In RL, episodes are considered agent-environment interactions from initial to final states. For example, in a car racing video game, you start the game (initial state) and play the game until it is over (final state). This is called an episode.

What is an episodic environment?

The episodic environment is also called the non-sequential environment. In an episodic environment, an agent’s current action will not affect a future action, whereas in a non-episodic environment, an agent’s current action will affect a future action and is also called the sequential environment.

What is episodic control?

Deep reinforcement learning methods attain super-human performance in a wide range of environments. We propose Neural Episodic Control: a deep reinforcement learning agent that is able to rapidly assimilate new experiences and act upon them.

What happens when discount factor is 1?

What is the role of the discount factor in RL? The discount factor, 𝛾, is a real value ∈ [0, 1], cares for the rewards agent achieved in the past, present, and future. If 𝛾 = 1, the agent cares for all future rewards.

What is the difference between an episodic and a continuous task?

An episodic task lasts a finite amount of time. For example, playing a single game of Go is an episodic task, which you win or lose. In an episodic task, there might be only a single reward, at the end of the task, and one option is to distribute the reward evenly across all actions taken in that episode. In a continuous task,…

What are episodic and continuous tasks in RL?

Episodic and continuous tasks. Episodic tasks are the tasks that have a terminal state (end). In RL, episodes are considered agent-environment interactions from initial to final states. For example, in a car racing video game, you start the game (initial state) and play the game until it is over (final state). This is called an episode.

What are some examples of reinforcement learning?

Till now we have been through many reinforcement learning examples, from on-policy to off-policy, di s crete state space to continuous state space.

What is the difference between a continuing task and a reward?

However, in a continuing task, the game never ends, thus the collected reward could go to infinity, it needs a term to restrain the estimation value, then there comes the average reward! As the step goes on, the average reward needs to be updated as well (Note that β is the learning rate dedicated for the reward update).