Contents

- 1 What is optimal policy reinforcement learning?
- 2 What is the policy in reinforcement learning?
- 3 Is MDP policy unique?
- 4 What is the definition of a policy in reinforcement learning?
- 5 How is softmax used in policy based reinforcement learning?
- 6 How to use TD in policy based reinforcement learning?
- 7 When to use Gaussian policy in reinforcement learning?

## What is optimal policy reinforcement learning?

Reinforcement learning is primarily concerned with how to obtain the optimal policy when such a model is not known in advance. The agent must interact with its environment directly to obtain information which, by means of an appropriate algorithm, can be processed to produce an optimal policy.

## What is the policy in reinforcement learning?

A policy is, therefore, a strategy that an agent uses in pursuit of goals. The policy dictates the actions that the agent takes as a function of the agent’s state and the environment.

## Is MDP policy unique?

A particular MDP may have multiple distinct optimal policies. Because of the Markov property, it can be shown that the optimal policy is a function of the current state, as assumed above.

## What is the definition of a policy in reinforcement learning?

2. The Definition of a Policy Reinforcement learning is a branch of machine learning dedicated to training agents to operate in an environment, in order to maximize their utility in the pursuit of some goals. Its underlying idea, states Russel, is that intelligence is an emergent property of the interaction between an agent and its environment.

## How is softmax used in policy based reinforcement learning?

The softmax Policy consists of a softmax function that converts output to a distribution of probabilities. Which means that it affects a probability for each possible action. Softmax is mostly used in the case discrete actions:

## How to use TD in policy based reinforcement learning?

One of the choices for the baseline is to compute the estimate of the state value, û (St,w), where w is a parameter vector learned by some methods such as Monte Carlo. Actor Critic algorithm uses TD in order to compute value function used as a critic. The critic is a state-value function.

## When to use Gaussian policy in reinforcement learning?

Gaussian policy is used in the case of continuous action space, for example when driving a car and you steer the wheels or press on the gas pedal, these are continuous actions because these are not few actions that you do since you you can (in theory) decide the rotation degree or the flow amount of gas.