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Found 9 papers, 5 papers with code
Successor-Predecessor Intrinsic Exploration
Here we focus on exploration with intrinsic rewards, where the agent transiently augments the external rewards with self-generated intrinsic rewards.
Unsupervised representation learning with recognition-parametrised probabilistic models
We introduce a new approach to probabilistic unsupervised learning based on the recognition-parametrised model (RPM): a normalised semi-parametric hypothesis class for joint distributions over observed and latent variables.
Structured Recognition for Generative Models with Explaining Away
A key goal of unsupervised learning is to go beyond density estimation and sample generation to reveal the structure inherent within observed data.
SEREN: Knowing When to Explore and When to Exploit
Efficient reinforcement learning (RL) involves a trade-off between "exploitative" actions that maximise expected reward and "explorative'" ones that sample unvisited states.
Learning State Representations via Retracing in Reinforcement Learning
We propose learning via retracing, a novel self-supervised approach for learning the state representation (and the associated dynamics model) for reinforcement learning tasks.
DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention
In this paper, we introduce a new generation of RL solvers that learn to minimise safety violations while maximising the task reward to the extent that can be tolerated by the safe policy.
Learning State Representations via Temporal Cycle-Consistency Constraint in Model-Based Reinforcement Learning
Representation learning is a popular approach for reinforcement learning (RL) tasks with partially observable Markov decision processes.
What About Inputing Policy in Value Function: Policy Representation and Policy-extended Value Function Approximator
We study Policy-extended Value Function Approximator (PeVFA) in Reinforcement Learning (RL), which extends conventional value function approximator (VFA) to take as input not only the state (and action) but also an explicit policy representation.
Prediction and Generalisation over Directed Actions by Grid Cells
Knowing how the effects of directed actions generalise to new situations (e. g. moving North, South, East and West, or turning left, right, etc.)
