Combining Model-Free Q-Ensembles and Model-Based Approaches for Informed Exploration

Q-Ensembles are a model-free approach where input images are fed into different Q-networks and exploration is driven by the assumption that uncertainty is proportional to the variance of the output Q-values obtained. They have been shown to perform relatively well compared to other exploration strategies. Further, model-based approaches, such as encoder-decoder models have been used successfully for next frame prediction given previous frames. This paper proposes to integrate the model-free Q-ensembles and model-based approaches with the hope of compounding the benefits of both and achieving superior exploration as a result. Results show that a model-based trajectory memory approach when combined with Q-ensembles produces superior performance when compared to only using Q-ensembles.

READ FULL TEXT
research
07/04/2018

Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion

Integrating model-free and model-based approaches in reinforcement learn...
research
11/12/2017

Automatic detection of alarm sounds in a noisy hospital environment using model and non-model based approaches

In the noisy acoustic environment of a Neonatal Intensive Care Unit (NIC...
research
03/22/2019

DQN with model-based exploration: efficient learning on environments with sparse rewards

We propose Deep Q-Networks (DQN) with model-based exploration, an algori...
research
06/06/2018

Model-free, Model-based, and General Intelligence

During the 60s and 70s, AI researchers explored intuitions about intelli...
research
11/03/2020

Goal recognition via model-based and model-free techniques

Goal recognition aims at predicting human intentions from a trace of obs...
research
09/20/2023

Practical Probabilistic Model-based Deep Reinforcement Learning by Integrating Dropout Uncertainty and Trajectory Sampling

This paper addresses the prediction stability, prediction accuracy and c...
research
05/22/2019

The Journey is the Reward: Unsupervised Learning of Influential Trajectories

Unsupervised exploration and representation learning become increasingly...

Please sign up or login with your details

Forgot password? Click here to reset