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Found 7 papers, 1 papers with code
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.
Single-Shot Pruning for Offline Reinforcement Learning
We leverage a fixed dataset to prune neural networks before the start of RL training.
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.
Off-Policy Adversarial Inverse Reinforcement Learning
Adversarial Imitation Learning (AIL) is a class of algorithms in Reinforcement learning (RL), which tries to imitate an expert without taking any reward from the environment and does not provide expert behavior directly to the policy training.
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.
