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Found 25 papers, 9 papers with code
Ignorance is Bliss: Robust Control via Information Gating
Informational parsimony -- i. e., using the minimal information required for a task, -- provides a useful inductive bias for learning representations that achieve better generalization by being robust to noise and spurious correlations.
Offline Policy Optimization in RL with Variance Regularizaton
Learning policies from fixed offline datasets is a key challenge to scale up reinforcement learning (RL) algorithms towards practical applications.
Representation Learning in Deep RL via Discrete Information Bottleneck
Several self-supervised representation learning methods have been proposed for reinforcement learning (RL) with rich observations.
Behavior Prior Representation learning for Offline Reinforcement Learning
Our method, Behavior Prior Representation (BPR), learns state representations with an easy-to-integrate objective based on behavior cloning of the dataset: we first learn a state representation by mimicking actions from the dataset, and then train a policy on top of the fixed representation, using any off-the-shelf Offline RL algorithm.
Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning
Goal-conditioned reinforcement learning (RL) is a promising direction for training agents that are capable of solving multiple tasks and reach a diverse set of objectives.
Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information
We find that contemporary representation learning techniques can fail on datasets where the noise is a complex and time dependent process, which is prevalent in practical applications.
Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models
In many sequential decision-making tasks, the agent is not able to model the full complexity of the world, which consists of multitudes of relevant and irrelevant information.
Importance of Empirical Sample Complexity Analysis for Offline Reinforcement Learning
We hypothesize that empirically studying the sample complexity of offline reinforcement learning (RL) is crucial for the practical applications of RL in the real world.
Offline Policy Optimization with Variance Regularization
Learning policies from fixed offline datasets is a key challenge to scale up reinforcement learning (RL) algorithms towards practical applications.
Marginalized State Distribution Entropy Regularization in Policy Optimization
Entropy regularization is used to get improved optimization performance in reinforcement learning tasks.
Doubly Robust Off-Policy Actor-Critic Algorithms for Reinforcement Learning
Furthermore, in cases where the reward function is stochastic that can lead to high variance, doubly robust critic estimation can improve performance under corrupted, stochastic reward signals, indicating its usefulness for robust and safe reinforcement learning.
Entropy Regularization with Discounted Future State Distribution in Policy Gradient Methods
In this work, we propose exploration in policy gradient methods based on maximizing entropy of the discounted future state distribution.
Off-Policy Policy Gradient Algorithms by Constraining the State Distribution Shift
This data distribution shift between current and past samples can significantly impact the performance of most modern off-policy based policy optimization algorithms.
Transfer Learning by Modeling a Distribution over Policies
Exploration and adaptation to new tasks in a transfer learning setup is a central challenge in reinforcement learning.
Transfer and Exploration via the Information Bottleneck
In new environments, this model can then identify novel subgoals for further exploration, guiding the agent through a sequence of potential decision states and through new regions of the state space.
InfoBot: Transfer and Exploration via the Information Bottleneck
In new environments, this model can then identify novel subgoals for further exploration, guiding the agent through a sequence of potential decision states and through new regions of the state space.
An Introduction to Deep Reinforcement Learning
Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning.
Exploring Restart Distributions
We consider the generic approach of using an experience memory to help exploration by adapting a restart distribution.
Bayesian Policy Gradients via Alpha Divergence Dropout Inference
Policy gradient methods have had great success in solving continuous control tasks, yet the stochastic nature of such problems makes deterministic value estimation difficult.
Alpha-Divergences in Variational Dropout
We investigate the use of alternative divergences to Kullback-Leibler (KL) in variational inference(VI), based on the Variational Dropout \cite{kingma2015}.
Bayesian Hypernetworks
We study Bayesian hypernetworks: a framework for approximate Bayesian inference in neural networks.
Deep Reinforcement Learning that Matters
In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL).
Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control
We investigate and discuss: the significance of hyper-parameters in policy gradients for continuous control, general variance in the algorithms, and reproducibility of reported results.
Deep Bayesian Active Learning with Image Data
In this paper we combine recent advances in Bayesian deep learning into the active learning framework in a practical way.
