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Temporal-difference Learning
Temporal difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate of the value function. These methods sample from the environment, like Monte Carlo methods, and perform updates based on current estimates, like dynamic programming methods. While Monte Carlo methods only adjust their estimates once the final outcome is known, TD methods adjust predictions to match later, more accurate, predictions about the future before the final outcome is known. This is a form of bootstrapping, as illustrated with the following example: Suppose you wish to predict the weather for Saturday, and you have some model that predicts Saturday's weather, given the weather of each day in the week. In the standard case, you would wait until Saturday and then adjust all your models. However, when it is, for example, Friday, you should have a pretty good idea of what the weather would be on Saturday – and thus be able ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Model-free (reinforcement Learning)
In reinforcement learning (RL), a model-free algorithm is an algorithm which does not estimate the ''transition probability distribution'' (and the ''reward function'') associated with the Markov decision process (MDP), which, in RL, represents the problem to be solved. The transition probability distribution (or transition model) and the reward function are often collectively called the "model" of the environment (or MDP), hence the name "model-free". A model-free RL algorithm can be thought of as an "explicit" Trial and error, trial-and-error algorithm. Typical examples of model-free algorithms include Monte Carlo method, Monte Carlo (MC) RL, State–action–reward–state–action, SARSA, and Q-learning. Monte Carlo estimation is a central component of many model-free RL algorithms. The MC learning algorithm is essentially an important branch of generalized policy iteration, which has two periodically alternating steps: policy evaluation (PEV) and policy improvement (PIM). In ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Neuroscience
Neuroscience is the scientific study of the nervous system (the brain, spinal cord, and peripheral nervous system), its functions, and its disorders. It is a multidisciplinary science that combines physiology, anatomy, molecular biology, developmental biology, cytology, psychology, physics, computer science, chemistry, medicine, statistics, and mathematical modeling to understand the fundamental and emergent properties of neurons, glia and neural circuits. The understanding of the biological basis of learning, memory, behavior, perception, and consciousness has been described by Eric Kandel as the "epic challenge" of the biological sciences. The scope of neuroscience has broadened over time to include different approaches used to study the nervous system at different scales. The techniques used by neuroscientists have expanded enormously, from molecular and cellular studies of individual neurons to imaging of sensory, motor and cognitive tasks in the brain. Hist ... [...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] |
Computational Neuroscience
Computational neuroscience (also known as theoretical neuroscience or mathematical neuroscience) is a branch of neuroscience which employs mathematics, computer science, theoretical analysis and abstractions of the brain to understand the principles that govern the development, structure, physiology and cognitive abilities of the nervous system. Computational neuroscience employs computational simulations to validate and solve mathematical models, and so can be seen as a sub-field of theoretical neuroscience; however, the two fields are often synonymous. The term mathematical neuroscience is also used sometimes, to stress the quantitative nature of the field. Computational neuroscience focuses on the description of biologically plausible neurons (and neural systems) and their physiology and dynamics, and it is therefore not directly concerned with biologically unrealistic models used in connectionism, control theory, cybernetics, quantitative psychology, machine le ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
State–action–reward–state–action
State–action–reward–state–action (SARSA) is an algorithm for learning a Markov decision process policy, used in the reinforcement learning area of machine learning. It was proposed by Rummery and Niranjan in a technical note with the name "Modified Connectionist Q-Learning" (MCQ-L). The alternative name SARSA, proposed by Rich Sutton, was only mentioned as a footnote. This name reflects the fact that the main function for updating the Q-value depends on the current state of the agent "S1", the action the agent chooses "A1", the reward "R2" the agent gets for choosing this action, the state "S2" that the agent enters after taking that action, and finally the next action "A2" the agent chooses in its new state. The acronym for the quintuple (St, At, Rt+1, St+1, At+1) is SARSA. Some authors use a slightly different convention and write the quintuple (St, At, Rt, St+1, At+1), depending on which time step the reward is formally assigned. The rest of the article uses the form ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Rescorla–Wagner Model
The Rescorla–Wagner model ("R-W") is a model of classical conditioning, in which learning is conceptualized in terms of associations between conditioned (CS) and unconditioned (US) stimuli. A strong CS-US association means that the CS signals predict the US. One might say that before conditioning, the subject is surprised by the US, but after conditioning, the subject is no longer surprised, because the CS predicts the coming of the US. The model casts the conditioning processes into discrete trials, during which stimuli may be either present or absent. The strength of prediction of the US on a trial can be represented as the summed associative strengths of all CSs present during the trial. This feature of the model represented a major advance over previous models, and it allowed a straightforward explanation of important experimental phenomena, most notably the blocking effect. Failures of the model have led to modifications, alternative models, and many additional findings. Th ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Q-learning
''Q''-learning is a reinforcement learning algorithm that trains an agent to assign values to its possible actions based on its current state, without requiring a model of the environment ( model-free). It can handle problems with stochastic transitions and rewards without requiring adaptations. For example, in a grid maze, an agent learns to reach an exit worth 10 points. At a junction, Q-learning might assign a higher value to moving right than left if right gets to the exit faster, improving this choice by trying both directions over time. For any finite Markov decision process, ''Q''-learning finds an optimal policy in the sense of maximizing the expected value of the total reward over any and all successive steps, starting from the current state. ''Q''-learning can identify an optimal action-selection policy for any given finite Markov decision process, given infinite exploration time and a partly random policy. "Q" refers to the function that the algorithm computes: th ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
PVLV
The primary value learned value (PVLV) model is a possible explanation for the reward-predictive firing properties of dopamine (DA) neurons. It simulates behavioral and neural data on Pavlovian conditioning and the midbrain The midbrain or mesencephalon is the uppermost portion of the brainstem connecting the diencephalon and cerebrum with the pons. It consists of the cerebral peduncles, tegmentum, and tectum. It is functionally associated with vision, hearing, mo ... dopaminergic neurons that fire in proportion to unexpected rewards. It is an alternative to the temporal-differences (TD) algorithm. It is used as part of Leabra. References Computational neuroscience Machine learning algorithms {{neuroscience-stub ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Schizophrenia
Schizophrenia () is a mental disorder characterized variously by hallucinations (typically, Auditory hallucination#Schizophrenia, hearing voices), delusions, thought disorder, disorganized thinking and behavior, and Reduced affect display, flat or inappropriate affect. Symptoms Prodrome, develop gradually and typically begin during young adulthood and rarely resolve. There is no objective diagnostic test; diagnosis is based on observed behavior, a psychiatric history that includes the person's reported experiences, and reports of others familiar with the person. For a diagnosis of schizophrenia, the described symptoms need to have been present for at least six months (according to the DSM-5) or one month (according to the ICD-11). Many people with schizophrenia have other mental disorders, especially mood disorder, mood, anxiety disorder, anxiety, and substance use disorders, substance use disorders, as well as obsessive–compulsive disorder (OCD). About 0.3% to 0.7% of peo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Dopamine
Dopamine (DA, a contraction of 3,4-dihydroxyphenethylamine) is a neuromodulatory molecule that plays several important roles in cells. It is an organic chemical of the catecholamine and phenethylamine families. It is an amine synthesized by removing a carboxyl group from a molecule of its precursor chemical, L-DOPA, which is synthesized in the brain and kidneys. Dopamine is also synthesized in plants and most animals. In the brain, dopamine functions as a neurotransmitter—a chemical released by neurons (nerve cells) to send signals to other nerve cells. The brain includes several distinct dopamine pathways, one of which plays a major role in the motivational component of reward-motivated behavior. The anticipation of most types of rewards increases the level of dopamine in the brain, and many addictive drugs increase dopamine release or block its reuptake into neurons following release. Other brain dopamine pathways are involved in motor control and in controllin ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Reward System
The reward system (the mesocorticolimbic circuit) is a group of neural structures responsible for incentive salience (i.e., "wanting"; desire or craving for a reward and motivation), associative learning (primarily positive reinforcement and classical conditioning), and positively-valenced emotions, particularly ones involving pleasure as a core component (e.g., joy, euphoria and ecstasy). Reward is the attractive and motivational property of a stimulus that induces appetitive behavior, also known as approach behavior, and consummatory behavior. A rewarding stimulus has been described as "any stimulus, object, event, activity, or situation that has the potential to make us approach and consume it is by definition a reward". In operant conditioning, rewarding stimuli function as positive reinforcers; however, the converse statement also holds true: positive reinforcers are rewarding. The reward system motivates animals to approach stimuli or engage in behaviour that increase ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Substantia Nigra
The substantia nigra (SN) is a basal ganglia structure located in the midbrain that plays an important role in reward and movement. ''Substantia nigra'' is Latin for "black substance", reflecting the fact that parts of the substantia nigra appear darker than neighboring areas due to high levels of neuromelanin in dopaminergic neurons. Parkinson's disease is characterized by the loss of dopaminergic neurons in the substantia nigra pars compacta. Although the substantia nigra appears as a continuous band in brain sections, anatomical studies have found that it actually consists of two parts with very different connections and functions: the pars compacta (SNpc) and the pars reticulata (SNpr). The pars compacta serves mainly as a projection to the basal ganglia circuit, supplying the striatum with dopamine. The pars reticulata conveys signals from the basal ganglia to numerous other brain structures. Structure The substantia nigra, along with four other nuclei, is ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
