scholarly journals Motor Cortex Encodes A Temporal Difference Reinforcement Learning Process

2018 ◽  
Author(s):  
Venkata S Aditya Tarigoppula ◽  
John S Choi ◽  
John P Hessburg ◽  
David B McNiel ◽  
Brandi T Marsh ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Motor Cortex ◽  
Learning Process ◽  
Neural Activity ◽  
Prediction Error ◽  
Value Function ◽  
Temporal Difference ◽  
Reward Prediction Error ◽  
Reward Prediction ◽  
The Value Function

AbstractTemporal difference reinforcement learning (TDRL) accurately models associative learning observed in animals, where they learn to associate outcome predicting environmental states, termed conditioned stimuli (CS), with the value of outcomes, such as rewards, termed unconditioned stimuli (US). A component of TDRL is the value function, which captures the expected cumulative future reward from a given state. The value function can be modified by changes in the animal’s knowledge, such as by the predictability of its environment. Here we show that primary motor cortical (M1) neurodynamics reflect a TD learning process, encoding a state value function and reward prediction error in line with TDRL. M1 responds to the delivery of reward, and shifts its value related response earlier in a trial, becoming predictive of an expected reward, when reward is predictable due to a CS. This is observed in tasks performed manually or observed passively, as well as in tasks without an explicit CS predicting reward, but simply with a predictable temporal structure, that is a predictable environment. M1 also encodes the expected reward value associated with a set of CS in a multiple reward level CS-US task. Here we extend the Microstimulus TDRL model, reported to accurately capture RL related dopaminergic activity, to account for M1 reward related neural activity in a multitude of tasks.Significance statementThere is a great deal of agreement between aspects of temporal difference reinforcement learning (TDRL) models and neural activity in dopaminergic brain centers. Dopamine is know to be necessary for sensorimotor learning induced synaptic plasticity in the motor cortex (M1), and thus one might expect to see the hallmarks of TDRL in M1, which we show here in the form of a state value function and reward prediction error during. We see these hallmarks even when a conditioned stimulus is not available, but the environment is predictable, during manual tasks with agency, as well as observational tasks without agency. This information has implications towards autonomously updating brain machine interfaces as others and we have proposed and published on.

scholarly journals Reward Prediction Error as an Exploration Objective in Deep RL

2020 ◽  
Author(s):  
Riley Simmons-Edler ◽  
Ben Eisner ◽  
Daniel Yang ◽  
Anthony Bisulco ◽  
Eric Mitchell ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Prediction Error ◽  
Value Function ◽  
Reward Prediction Error ◽  
A Value ◽  
Reward Prediction ◽  
Exploration Behavior ◽  
Q Function ◽  
Baseline State ◽  
Better Than

A major challenge in reinforcement learning is exploration, when local dithering methods such as epsilon-greedy sampling are insufficient to solve a given task. Many recent methods have proposed to intrinsically motivate an agent to seek novel states, driving the agent to discover improved reward. However, while state-novelty exploration methods are suitable for tasks where novel observations correlate well with improved reward, they may not explore more efficiently than epsilon-greedy approaches in environments where the two are not well-correlated. In this paper, we distinguish between exploration tasks in which seeking novel states aids in finding new reward, and those where it does not, such as goal-conditioned tasks and escaping local reward maxima. We propose a new exploration objective, maximizing the reward prediction error (RPE) of a value function trained to predict extrinsic reward. We then propose a deep reinforcement learning method, QXplore, which exploits the temporal difference error of a Q-function to solve hard exploration tasks in high-dimensional MDPs. We demonstrate the exploration behavior of QXplore on several OpenAI Gym MuJoCo tasks and Atari games and observe that QXplore is comparable to or better than a baseline state-novelty method in all cases, outperforming the baseline on tasks where state novelty is not well-correlated with improved reward.

scholarly journals Reward prediction error in the ERP following unconditioned aversive stimuli

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Harry J. Stewardson ◽  
Thomas D. Sambrook
Keyword(s):  
Reinforcement Learning ◽  
Prediction Error ◽  
Dopamine Neurons ◽  
Prediction Errors ◽  
Temporal Difference ◽  
Dopamine System ◽  
Reward Prediction Error ◽  
Reward Prediction ◽  
Midbrain Dopamine ◽  
Human Participants

AbstractReinforcement learning in humans and other animals is driven by reward prediction errors: deviations between the amount of reward or punishment initially expected and that which is obtained. Temporal difference methods of reinforcement learning generate this reward prediction error at the earliest time at which a revision in reward or punishment likelihood is signalled, for example by a conditioned stimulus. Midbrain dopamine neurons, believed to compute reward prediction errors, generate this signal in response to both conditioned and unconditioned stimuli, as predicted by temporal difference learning. Electroencephalographic recordings of human participants have suggested that a component named the feedback-related negativity (FRN) is generated when this signal is carried to the cortex. If this is so, the FRN should be expected to respond equivalently to conditioned and unconditioned stimuli. However, very few studies have attempted to measure the FRN’s response to unconditioned stimuli. The present study attempted to elicit the FRN in response to a primary aversive stimulus (electric shock) using a design that varied reward prediction error while holding physical intensity constant. The FRN was strongly elicited, but earlier and more transiently than typically seen, suggesting that it may incorporate other processes than the midbrain dopamine system.

scholarly journals Differential reinforcement encoding along the hippocampal long axis helps resolve the explore–exploit dilemma

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Alexandre Y. Dombrovski ◽  
Beatriz Luna ◽  
Michael N. Hallquist
Keyword(s):  
Reinforcement Learning ◽  
Prediction Error ◽  
Differential Reinforcement ◽  
Cognitive Maps ◽  
Learning Task ◽  
Natural Environments ◽  
Reward Prediction Error ◽  
Reward Function ◽  
Reward Prediction ◽  
Reward Information

Abstract When making decisions, should one exploit known good options or explore potentially better alternatives? Exploration of spatially unstructured options depends on the neocortex, striatum, and amygdala. In natural environments, however, better options often cluster together, forming structured value distributions. The hippocampus binds reward information into allocentric cognitive maps to support navigation and foraging in such spaces. Here we report that human posterior hippocampus (PH) invigorates exploration while anterior hippocampus (AH) supports the transition to exploitation on a reinforcement learning task with a spatially structured reward function. These dynamics depend on differential reinforcement representations in the PH and AH. Whereas local reward prediction error signals are early and phasic in the PH tail, global value maximum signals are delayed and sustained in the AH body. AH compresses reinforcement information across episodes, updating the location and prominence of the value maximum and displaying goal cell-like ramping activity when navigating toward it.

scholarly journals Neural correlates of weighted reward prediction error during reinforcement learning classify response to cognitive behavioral therapy in depression

2019 ◽  
Vol 5 (7) ◽  
pp. eaav4962 ◽  
Author(s):  
Filippo Queirazza ◽  
Elsa Fouragnan ◽  
J. Douglas Steele ◽  
Jonathan Cavanagh ◽  
Marios G. Philiastides
Keyword(s):  
Cognitive Behavioral Therapy ◽  
Treatment Response ◽  
Neural Activity ◽  
Prediction Error ◽  
Behavioral Therapy ◽  
Predictive Biomarkers ◽  
Cognitive Behavioral ◽  
Reward Prediction Error ◽  
Feedback Information ◽  
Reward Prediction

While cognitive behavioral therapy (CBT) is an effective treatment for major depressive disorder, only up to 45% of depressed patients will respond to it. At present, there is no clinically viable neuroimaging predictor of CBT response. Notably, the lack of a mechanistic understanding of treatment response has hindered identification of predictive biomarkers. To obtain mechanistically meaningful fMRI predictors of CBT response, we capitalize on pretreatment neural activity encoding a weighted reward prediction error (RPE), which is implicated in the acquisition and processing of feedback information during probabilistic learning. Using a conventional mass-univariate fMRI analysis, we demonstrate that, at the group level, responders exhibit greater pretreatment neural activity encoding a weighted RPE in the right striatum and right amygdala. Crucially, using multivariate methods, we show that this activity offers significant out-of-sample classification of treatment response. Our findings support the feasibility and validity of neurocomputational approaches to treatment prediction in psychiatry.

scholarly journals Dopamine mediates the bidirectional update of interval timing

2021 ◽  
Author(s):  
Anthony M.V. Jakob ◽  
John G Mikhael ◽  
Allison E Hamilos ◽  
John A Assad ◽  
Samuel J Gershman
Keyword(s):  
Reinforcement Learning ◽  
Prediction Error ◽  
Interval Timing ◽  
Substantia Nigra Pars Compacta ◽  
Subjective Time ◽  
Reward Prediction Error ◽  
Learning Tasks ◽  
Reward Prediction ◽  
Reward Delivery ◽  
Speed Up

The role of dopamine as a reward prediction error signal in reinforcement learning tasks has been well-established over the past decades. Recent work has shown that the reward prediction error interpretation can also account for the effects of dopamine on interval timing by controlling the speed of subjective time. According to this theory, the timing of the dopamine signal relative to reward delivery dictates whether subjective time speeds up or slows down: Early DA signals speed up subjective time and late signals slow it down. To test this bidirectional prediction, we reanalyzed measurements of dopaminergic neurons in the substantia nigra pars compacta of mice performing a self-timed movement task. Using the slope of ramping dopamine activity as a read-out of subjective time speed, we found that trial-by-trial changes in the slope could be predicted from the timing of dopamine activity on the previous trial. This result provides a key piece of evidence supporting a unified computational theory of reinforcement learning and interval timing.

scholarly journals Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning

2020 ◽  
Vol 34 (04) ◽  
pp. 3741-3748
Author(s):  
Kristopher De Asis ◽  
Alan Chan ◽  
Silviu Pitis ◽  
Richard Sutton ◽  
Daniel Graves
Keyword(s):  
Reinforcement Learning ◽  
Function Approximation ◽  
Value Function ◽  
Temporal Difference ◽  
Value Functions ◽  
Difference Methods ◽  
Td Methods ◽  
The Stability ◽  
The Value Function ◽  
Temporal Difference Methods

We explore fixed-horizon temporal difference (TD) methods, reinforcement learning algorithms for a new kind of value function that predicts the sum of rewards over a fixed number of future time steps. To learn the value function for horizon h, these algorithms bootstrap from the value function for horizon h−1, or some shorter horizon. Because no value function bootstraps from itself, fixed-horizon methods are immune to the stability problems that plague other off-policy TD methods using function approximation (also known as “the deadly triad”). Although fixed-horizon methods require the storage of additional value functions, this gives the agent additional predictive power, while the added complexity can be substantially reduced via parallel updates, shared weights, and n-step bootstrapping. We show how to use fixed-horizon value functions to solve reinforcement learning problems competitively with methods such as Q-learning that learn conventional value functions. We also prove convergence of fixed-horizon temporal difference methods with linear and general function approximation. Taken together, our results establish fixed-horizon TD methods as a viable new way of avoiding the stability problems of the deadly triad.

scholarly journals Behavioural and computational evidence for memory consolidation biased by reward-prediction errors

2019 ◽  
Author(s):  
Emma L. Roscow ◽  
Matthew W. Jones ◽  
Nathan F. Lepora
Keyword(s):  
Reinforcement Learning ◽  
Prediction Error ◽  
Learning Task ◽  
Male Rats ◽  
Adaptive Behaviour ◽  
Prediction Errors ◽  
Reward Prediction Error ◽  
Reward Prediction ◽  
Per Se ◽  
Reinforcement Learning Model

AbstractNeural activity encoding recent experiences is replayed during sleep and rest to promote consolidation of the corresponding memories. However, precisely which features of experience influence replay prioritisation to optimise adaptive behaviour remains unclear. Here, we trained adult male rats on a novel maze-based rein-forcement learning task designed to dissociate reward outcomes from reward-prediction errors. Four variations of a reinforcement learning model were fitted to the rats’ behaviour over multiple days. Behaviour was best predicted by a model incorporating replay biased by reward-prediction error, compared to the same model with no replay; random replay or reward-biased replay produced poorer predictions of behaviour. This insight disentangles the influences of salience on replay, suggesting that reinforcement learning is tuned by post-learning replay biased by reward-prediction error, not by reward per se. This work therefore provides a behavioural and theoretical toolkit with which to measure and interpret replay in striatal, hippocampal and neocortical circuits.

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