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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 10326–10350 of 15113 papers

TitleStatusHype
Efficient PAC Reinforcement Learning in Regular Decision Processes—0
Efficient Performance Bounds for Primal-Dual Reinforcement Learning from Demonstrations—0
Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning—0
Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning—0
Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents—0
Efficient Policy Learning for Non-Stationary MDPs under Adversarial Manipulation—0
Efficient Poverty Mapping using Deep Reinforcement Learning—0
Efficient Preference-Based Reinforcement Learning Using Learned Dynamics Models—0
Reinforcement Learning for Causal Discovery without Acyclicity Constraints—0
Efficient Reinforcement Learning Experimentation in PyTorch—0
Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging Simple Rules—0
Efficient Reinforcement Learning for Unsupervised Controlled Text Generation—0
Efficient Reinforcement Learning for High Dimensional Linear Quadratic Systems—0
Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systems—0
Efficient Reinforcement Learning from Demonstration Using Local Ensemble and Reparameterization with Split and Merge of Expert Policies—0
Efficient Reinforcement Learning in Deterministic Systems with Value Function Generalization—0
Efficient Reinforcement Learning in Factored MDPs with Application to Constrained RL—0
Efficient Reinforcement Learning in Resource Allocation Problems Through Permutation Invariant Multi-task Learning—0
Efficient Reinforcement Learning via Initial Pure Exploration—0
Extracting Heuristics from Large Language Models for Reward Shaping in Reinforcement Learning—0
Efficient Reinforcement Learning with Large Language Model Priors—0
Efficient Representation for Electric Vehicle Charging Station Operations using Reinforcement Learning—0
Efficient Representations of Object Geometry for Reinforcement Learning of Interactive Grasping Policies—0
Efficient Reservoir Management through Deep Reinforcement Learning—0
Efficient Residual Learning with Mixture-of-Experts for Universal Dexterous Grasping—0
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Benchmark Results

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