Value prediction
15 papers with code • 1 benchmarks • 0 datasets
Most implemented papers
Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble
corl-team/CORL
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NeurIPS 2021
However, prior methods typically require accurate estimation of the behavior policy or sampling from OOD data points, which themselves can be a non-trivial problem.
Value Prediction Network
junhyukoh/value-prediction-network
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NeurIPS 2017
This paper proposes a novel deep reinforcement learning (RL) architecture, called Value Prediction Network (VPN), which integrates model-free and model-based RL methods into a single neural network.
TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning
To address these challenges, we propose TreeQN, a differentiable, recursive, tree-structured model that serves as a drop-in replacement for any value function network in deep RL with discrete actions.
ACE: An Actor Ensemble Algorithm for Continuous Control with Tree Search
In this paper, we propose an actor ensemble algorithm, named ACE, for continuous control with a deterministic policy in reinforcement learning.
Code Prediction by Feeding Trees to Transformers
facebookresearch/code-prediction-transformer
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We provide comprehensive experimental evaluation of our proposal, along with alternative design choices, on a standard Python dataset, as well as on a Python corpus internal to Facebook.
Spatial Action Maps for Mobile Manipulation
jimmyyhwu/spatial-action-maps
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Typical end-to-end formulations for learning robotic navigation involve predicting a small set of steering command actions (e. g., step forward, turn left, turn right, etc.)
PIVEN: A Deep Neural Network for Prediction Intervals with Specific Value Prediction
elisim/piven
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Improving the robustness of neural nets in regression tasks is key to their application in multiple domains.
timeXplain -- A Framework for Explaining the Predictions of Time Series Classifiers
Modern time series classifiers display impressive predictive capabilities, yet their decision-making processes mostly remain black boxes to the user.
DATE: Dual Attentive Tree-aware Embedding for Customs Fraud Detection
Roytsai27/Dual-Attentive-Tree-aware-Embedding
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KDD 2020
Intentional manipulation of invoices that lead to undervaluation of trade goods is the most common type of customs fraud to avoid ad valorem duties and taxes.
Learning State Representations from Random Deep Action-conditional Predictions
Hwhitetooth/random_gvfs
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NeurIPS 2021
Our main contribution in this work is an empirical finding that random General Value Functions (GVFs), i. e., deep action-conditional predictions -- random both in what feature of observations they predict as well as in the sequence of actions the predictions are conditioned upon -- form good auxiliary tasks for reinforcement learning (RL) problems.
