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Imitation Learning
Imitation learning is a paradigm in reinforcement learning, where an agent learns to perform a task by supervised learning from expert demonstrations. It is also called learning from demonstration and apprenticeship learning. It has been applied to underactuated robotics, self-driving cars, quadcopter navigation, helicopter aerobatics, and locomotion. Approaches Expert demonstrations are recordings of an expert performing the desired task, often collected as state-action pairs (o_t^*, a_t^*). Behavior Cloning Behavior Cloning (BC) is the most basic form of imitation learning. Essentially, it uses supervised learning to train a policy \pi_\theta such that, given an observation o_t, it would output an action distribution \pi_\theta(\cdot , o_t) that is approximately the same as the action distribution of the experts. BC is susceptible to distribution shift. Specifically, if the trained policy differs from the expert policy, it might find itself straying from expert trajector ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Reinforcement Learning
Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input-output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead, the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge) with the goal of maximizing the cumulative reward (the feedback of which might be incomplete or delayed). The search for this balance is known as the exploration–exploitation dilemma. The environment is typically stated in the form of a Markov decision process (MDP), as many reinforcement learning algorithms use dyn ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Supervised Learning
In machine learning, supervised learning (SL) is a paradigm where a Statistical model, model is trained using input objects (e.g. a vector of predictor variables) and desired output values (also known as a ''supervisory signal''), which are often human-made labels. The training process builds a function that maps new data to expected output values. An optimal scenario will allow for the algorithm to accurately determine output values for unseen instances. This requires the learning algorithm to Generalization (learning), generalize from the training data to unseen situations in a reasonable way (see inductive bias). This statistical quality of an algorithm is measured via a ''generalization error''. Steps to follow To solve a given problem of supervised learning, the following steps must be performed: # Determine the type of training samples. Before doing anything else, the user should decide what kind of data is to be used as a Training, validation, and test data sets, trainin ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Distribution Shift
Distribution may refer to: Mathematics *Distribution (mathematics), generalized functions used to formulate solutions of partial differential equations *Probability distribution, the probability of a particular value or value range of a variable **Cumulative distribution function, in which the probability of being no greater than a particular value is a function of that value *Frequency distribution, a list of the values recorded in a sample * Inner distribution, and outer distribution, in coding theory *Distribution (differential geometry), a subset of the tangent bundle of a manifold * Distributed parameter system, systems that have an infinite-dimensional state-space * Distribution of terms, a situation in which all members of a category are accounted for *Distributivity, a property of binary operations that generalises the distributive law from elementary algebra *Distribution (number theory) *Distribution problems, a common type of problems in combinatorics where the goa ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
ALVINN
Navlab is a series of autonomous and semi-autonomous vehicles developed by teams from The Robotics Institute at the School of Computer Science, Carnegie Mellon University. Later models were produced under a new department created specifically for the research called "The Carnegie Mellon University Navigation Laboratory". Navlab 5 notably steered itself almost all the way from Pittsburgh to San Diego. History Research on computer controlled vehicles began at Carnegie Mellon in 1984 as part of the DARPA Strategic Computing Initiative and production of the first vehicle, Navlab 1, began in 1986. Navlab 1 burned in 1989 when conditioning system leaked liquid onto the computers. Applications The vehicles in the Navlab series have been designed for varying purposes, "... off-road scouting; automated highways; run-off-road collision prevention; and driver assistance for maneuvering in crowded city environments. Our current work involves pedestrian detection, surround sensing, a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Decision Transformer Architecture
Decision may refer to: Law and politics *Judgment (law), as the outcome of a legal case *Landmark decision, the outcome of a case that sets a legal precedent * ''Per curiam'' decision, by a court with multiple judges Books * ''Decision'' (novel), a 1983 political novel by Allen Drury *''Decisions'', a 1997 poetry collection by Chimamanda Ngozi Adichie Sports *Decision (baseball), a statistical credit earned by a baseball pitcher * Decisions in combat sports *Decisions (professional wrestling), by which a wrestler scores a point against his opponent Film and TV * ''Decision'' (TV series), an American anthology TV series Music Albums * ''Decisions'' (George Adams and Don Pullen album), 1984 * ''Decisions'' (The Winans album), 1987 * ''Decided'' (mixtape) by YoungBoy Never Broke Again, 2018 Songs * "Decisions" (song), by Borgore featuring Miley Cyrus *"Decisions", song by The Expression Tom Haran 1983 *"Decisions", song by Van McCoy 1979 *"Decision", a song by Busta Rhymes from ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Transformer (deep Learning Architecture)
The transformer is a deep learning architecture based on the multi-head attention mechanism, in which text is converted to numerical representations called tokens, and each token is converted into a vector via lookup from a word embedding table. At each layer, each token is then contextualized within the scope of the context window with other (unmasked) tokens via a parallel multi-head attention mechanism, allowing the signal for key tokens to be amplified and less important tokens to be diminished. Transformers have the advantage of having no recurrent units, therefore requiring less training time than earlier recurrent neural architectures (RNNs) such as long short-term memory (LSTM). Later variations have been widely adopted for training large language models (LLM) on large (language) datasets. The modern version of the transformer was proposed in the 2017 paper " Attention Is All You Need" by researchers at Google. Transformers were first developed as an improvement ov ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Neural Scaling Law
In machine learning, a neural scaling law is an empirical scaling law that describes how Neural network (machine learning), neural network performance changes as key factors are scaled up or down. These factors typically include the number of parameters, Training, validation, and test data sets, training dataset size, and training cost. Introduction In general, a deep learning model can be characterized by four parameters: model size, training dataset size, training cost, and the post-training error rate (e.g., the test set error rate). Each of these variables can be defined as a real number, usually written as N, D, C, L (respectively: parameter count, dataset size, computing cost, and Loss function, loss). A neural scaling law is a theoretical or empirical Empirical statistical laws, statistical law between these parameters. There are also other parameters with other scaling laws. Size of the model In most cases, the model's size is simply the number of parameters. However, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Atari Games
Atari Games Corporation was an American producer of arcade video games, active from 1985 to 1999, then as Midway Games West Inc. until 2003. It was formed when the coin-operated video game division of Atari, Inc. was transferred by its owner Warner Communications to a joint venture with Namco, being one of several successor companies to use the name Atari. The company developed and published games for arcades under the Atari brand, and across consumer home systems such as the Commodore 16, Commodore 64, Game Boy, Nintendo Entertainment System (NES) and others using the Tengen (company), Tengen label for legal reasons. Some of the games Atari Games had developed include ''Tetris (Atari Games), Tetris, Road Runner (video game), Road Runner, RoadBlasters,'' ''Primal Rage, Hard Drivin''' and San Francisco Rush: Extreme Racing, ''San Francisco Rush''. Atari Games effectively operated independently from 1987, when Namco sold its controlling stake, until Time Warner reassumed full own ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Inverse Reinforcement Learning
Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input-output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead, the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge) with the goal of maximizing the cumulative reward (the feedback of which might be incomplete or delayed). The search for this balance is known as the exploration–exploitation dilemma. The environment is typically stated in the form of a Markov decision process (MDP), as many reinforcement learning algorithms use dynami ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Generative Adversarial Network
A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative artificial intelligence. The concept was initially developed by Ian Goodfellow and his colleagues in June 2014. In a GAN, two neural networks compete with each other in the form of a zero-sum game, where one agent's gain is another agent's loss. Given a training set, this technique learns to generate new data with the same statistics as the training set. For example, a GAN trained on photographs can generate new photographs that look at least superficially authentic to human observers, having many realistic characteristics. Though originally proposed as a form of generative model for unsupervised learning, GANs have also proved useful for semi-supervised learning, fully supervised learning, and reinforcement learning. The core idea of a GAN is based on the "indirect" training through the discriminator, another neural network that can tell ho ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
