What is a trajectory in reinforcement learning?

In reinforcement learning terminology, a trajectory τ is the path of the agent through the state space up until the horizon H. The goal of an on-policy algorithm is to maximize the expected reward of the agent over trajectories.

What is a 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.

What is on-policy and off policy reinforcement learning?

On-policy methods attempt to evaluate or improve the policy that is used to make decisions. In contrast, off-policy methods evaluate or improve a policy different from that used to generate the data.

What is policy and value in reinforcement learning?

For this purpose there are two concepts in Reinforcement Learning, each answering one of the questions. The value function covers the part of evaluating the current situation of the agent in the environment and the policy, which describes the decision-making process of the agent.

What is the difference between on policy and off policy learning?

“An off-policy learner learns the value of the optimal policy independently of the agent’s actions. Q-learning is an off-policy learner. An on-policy learner learns the value of the policy being carried out by the agent including the exploration steps.”

What is off-policy in reinforcement learning?

Q-learning is an off-policy algorithm (Sutton & Barto, 1998), meaning the target can be computed without consideration of how the experience was generated. In principle, off- policy reinforcement learning algorithms are able to learn from data collected by any behavioral policy.

What is the difference between policy iteration and value iteration?

In policy iteration, we start with a fixed policy. Conversely, in value iteration, we begin by selecting the value function. Then, in both algorithms, we iteratively improve until we reach convergence.

What is the difference between supervised and unsupervised learning and reinforcement learning?

Supervised Learning predicts based on a class type. Unsupervised Learning discovers underlying patterns. Whereas, Unsupervised Learning explore patterns and predict the output. Reinforcement Learning follows a trial and error method.

What is the difference between policy and value functions?

The value function covers the part of evaluating the current situation of the agent in the environment and the policy, which describes the decision-making process of the agent.

What is policy gradient in reinforcement learning?

Policy gradient methods are a type of reinforcement learning techniques that rely upon optimizing parametrized policies with respect to the expected return (long-term cumulative reward) by gradient descent.

What is the difference between trajectory and on-policy?

According to this answer over Quora: In reinforcement learning terminology, a trajectory τ is the path of the agent through the state space up until the horizon H. The goal of an on-policy algorithm is to maximize the expected reward of the agent over trajectories.

How does a reinforcement learning agent interact with its environment?

A reinforcement learning agent interacts with its environment in discrete time steps. At each time t, the agent receives an observation , which typically includes the reward . It then chooses an action from the set of available actions, which is subsequently sent to the environment.

What is the difference between RL and a trajectory?

A trajectory ist just a sequence of states and actions. In RL, the goal is to maximize the reward, by finding the right trajectories. This means maximizing not immediate reward (caused by one action from a state), but cumulative reward (all states and actions: trajectory)

What is rereinforcement learning?

Reinforcement learning. Reinforcement learning is an area of Machine Learning. Reinforcement. It is about taking suitable action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation.