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  2. Tsiolkovsky rocket equation - Wikipedia

    en.wikipedia.org/wiki/Tsiolkovsky_rocket_equation

    A rocket's required mass ratio as a function of effective exhaust velocity ratio. The classical rocket equation, or ideal rocket equation is a mathematical equation that describes the motion of vehicles that follow the basic principle of a rocket: a device that can apply acceleration to itself using thrust by expelling part of its mass with high velocity and can thereby move due to the ...

  3. Reinforcement learning - Wikipedia

    en.wikipedia.org/wiki/Reinforcement_learning

    Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent ought to take actions in a dynamic environment in order to maximize the cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and ...

  4. Markov decision process - Wikipedia

    en.wikipedia.org/wiki/Markov_decision_process

    The "Markov" in "Markov decision process" refers to the underlying structure of state transitions that still follow the Markov property. The process is called a "decision process" because it involves making decisions that influence these state transitions, extending the concept of a Markov chain into the realm of decision-making under uncertainty.

  5. Mountain car problem - Wikipedia

    en.wikipedia.org/wiki/Mountain_car_problem

    The mountain car problem. Mountain Car, a standard testing domain in Reinforcement learning, is a problem in which an under-powered car must drive up a steep hill. Since gravity is stronger than the car's engine, even at full throttle, the car cannot simply accelerate up the steep slope. The car is situated in a valley and must learn to ...

  6. Reinforcement learning from human feedback - Wikipedia

    en.wikipedia.org/wiki/Reinforcement_learning...

    Human feedback is commonly collected by prompting humans to rank instances of the agent's behavior. [13] [15] [16] These rankings can then be used to score outputs, for example, using the Elo rating system, which is an algorithm for calculating the relative skill levels of players in a game based only on the outcome of each game. [3]

  7. Model-free (reinforcement learning) - Wikipedia

    en.wikipedia.org/wiki/Model-free_(reinforcement...

    t. e. In reinforcement learning (RL), a model-free algorithm (as opposed to a model-based one) is an algorithm which does not estimate the transition probability distribution (and the reward function) associated with the Markov decision process (MDP), [1] which, in RL, represents the problem to be solved. The transition probability distribution ...

  8. Learning rate - Wikipedia

    en.wikipedia.org/wiki/Learning_rate

    v. t. e. In machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum of a loss function. [1] Since it influences to what extent newly acquired information overrides old information, it metaphorically represents the speed at ...

  9. Monte Carlo tree search - Wikipedia

    en.wikipedia.org/wiki/Monte_Carlo_tree_search

    This step is sometimes also called playout or rollout. A playout may be as simple as choosing uniform random moves until the game is decided (for example in chess, the game is won, lost, or drawn). Backpropagation: Use the result of the playout to update information in the nodes on the path from C to R. Step of Monte Carlo tree search.

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