Value prediction

10 papers with code • 1 benchmarks • 0 datasets

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Most implemented papers

Value Prediction Network

junhyukoh/value-prediction-network 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

oxwhirl/treeqn ICLR 2018

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.

A Closer Look at Deep Policy Gradients

alex-lindt/variance_n_step_actor_critic ICLR 2020

We study how the behavior of deep policy gradient algorithms reflects the conceptual framework motivating their development.

ACE: An Actor Ensemble Algorithm for Continuous Control with Tree Search

ShangtongZhang/DeepRL 6 Nov 2018

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 30 Mar 2020

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 20 Apr 2020

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 9 Jun 2020

Improving the robustness of neural nets in regression tasks is key to their application in multiple domains.

DATE: Dual Attentive Tree-aware Embedding for Customs Fraud Detection

Roytsai27/Dual-Attentive-Tree-aware-Embedding 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 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.

On the Estimation Bias in Double Q-Learning

stilwell-git/doubly-bounded-q-learning NeurIPS 2021

Double Q-learning is a classical method for reducing overestimation bias, which is caused by taking maximum estimated values in the Bellman operation.