SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

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Showing 4726–4750 of 15113 papers

TitleStatusHype
Safe Reinforcement Learning From Pixels Using a Stochastic Latent RepresentationCode0
Policy Gradients for Probabilistic Constrained Reinforcement Learning—0
GFlowNets and variational inferenceCode0
EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model—0
Robust Bayesian optimization with reinforcement learned acquisition functions—0
Comparing BERT-based Reward Functions for Deep Reinforcement Learning in Machine Translation—0
Can Data Diversity Enhance Learning Generalization?—0
Parsing Natural Language into Propositional and First-Order Logic with Dual Reinforcement Learning—0
Bayesian Q-learning With Imperfect Expert Demonstrations—0
Learning-Based Adaptive Optimal Control of Linear Time-Delay Systems: A Policy Iteration Approach—0
Zero-Shot Policy Transfer with Disentangled Task Representation of Meta-Reinforcement Learning—0
Deep Intrinsically Motivated Exploration in Continuous ControlCode1
Towards a Fully Autonomous UAV Controller for Moving Platform Detection and Landing—0
Efficiently Learning Small Policies for Locomotion and Manipulation—0
Efficient LSTM Training with Eligibility Traces—0
Improving Policy Learning via Language Dynamics DistillationCode0
ASPiRe:Adaptive Skill Priors for Reinforcement Learning—0
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning—0
B2RL: An open-source Dataset for Building Batch Reinforcement LearningCode0
Reward Shaping for User Satisfaction in a REINFORCE Recommender—0
Bounded Robustness in Reinforcement Learning via Lexicographic Objectives—0
RL-MD: A Novel Reinforcement Learning Approach for DNA Motif Discovery—0
Programmable Control of Ultrasound Swarmbots through Reinforcement Learning—0
S2P: State-conditioned Image Synthesis for Data Augmentation in Offline Reinforcement LearningCode0
The Role of Time Delay in Sim2real Transfer of Reinforcement Learning for Cyber-Physical Systems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified