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Found 7 papers, 3 papers with code
Episodic Reinforcement Learning with Expanded State-reward Space
As a result, our method is able to simultaneously achieve the full utilization of retrieval information and the better evaluation of state values by a Temporal Difference (TD) loss.
Sequential Action-Induced Invariant Representation for Reinforcement Learning
Specifically, we propose a Sequential Action--induced invariant Representation (SAR) method, in which the encoder is optimized by an auxiliary learner to only preserve the components that follow the control signals of sequential actions, so the agent can be induced to learn the robust representation against distractions.
Low-Light Image Enhancement with Multi-Stage Residue Quantization and Brightness-Aware Attention
Deep learning-based LLIE methods focus on learning a mapping function between low-light images and normal-light images that outperforms conventional LLIE methods.
Online Learning and Planning in Partially Observable Domains without Prior Knowledge
How an agent can act optimally in stochastic, partially observable domains is a challenge problem, the standard approach to address this issue is to learn the domain model firstly and then based on the learned model to find the (near) optimal policy.
Combining Offline Models and Online Monte-Carlo Tree Search for Planning from Scratch
Planning in stochastic and partially observable environments is a central issue in artificial intelligence.
Conformation Clustering of Long MD Protein Dynamics with an Adversarial Autoencoder
Multiple protein states and a large number of microstates associated with folding and with the function of the protein can be observed as conformations sampled in the trajectories.
Selecting Bases in Spectral learning of Predictive State Representations via Model Entropy
Predictive State Representations (PSRs) are powerful techniques for modelling dynamical systems, which represent a state as a vector of predictions about future observable events (tests).
