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 6801–6850 of 15113 papers

TitleStatusHype
Efficient decorrelation of features using Gramian in Reinforcement Learning—0
Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learning—0
Efficient Domain Coverage for Vehicles with Second-Order Dynamics via Multi-Agent Reinforcement Learning—0
Efficient Drone Mobility Support Using Reinforcement Learning—0
Efficient Duple Perturbation Robustness in Low-rank MDPs—0
Efficient Dynamics Modeling in Interactive Environments with Koopman Theory—0
Efficient Embedding of Semantic Similarity in Control Policies via Entangled Bisimulation—0
Efficient entity-based reinforcement learning—0
Efficient Entropy for Policy Gradient with Multidimensional Action Space—0
Efficient Evaluation of Natural Stochastic Policies in Offline Reinforcement Learning—0
Efficient Exploration and Value Function Generalization in Deterministic Systems—0
Efficient Exploration for Model-based Reinforcement Learning with Continuous States and Actions—0
Efficient Exploration in Constrained Environments with Goal-Oriented Reference Path—0
Efficient Exploration in Deep Reinforcement Learning: A Novel Bayesian Actor-Critic Algorithm—0
Efficient Exploration in Resource-Restricted Reinforcement Learning—0
Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization—0
Efficient Exploration through Intrinsic Motivation Learning for Unsupervised Subgoal Discovery in Model-Free Hierarchical Reinforcement Learning—0
Efficient Exploration Using Extra Safety Budget in Constrained Policy Optimization—0
Efficient Exploration via Epistemic-Risk-Seeking Policy Optimization—0
Efficient Implementation of Reinforcement Learning over Homomorphic Encryption—0
Uncertainty Quantification and Exploration for Reinforcement Learning—0
Efficient Inference and Exploration for Reinforcement Learning—0
Efficient Learning of High Level Plans from Play—0
Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization—0
Efficient Learning of Voltage Control Strategies via Model-based Deep Reinforcement Learning—0
Efficient LSTM Training with Eligibility Traces—0
Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning—0
Efficiently Learning Small Policies for Locomotion and Manipulation—0
Efficiently Training On-Policy Actor-Critic Networks in Robotic Deep Reinforcement Learning with Demonstration-like Sampled Exploration—0
Efficient meta reinforcement learning via meta goal generation—0
MGHRL: Meta Goal-generation for Hierarchical Reinforcement Learning—0
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation—0
Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning—0
Efficient model-based reinforcement learning for approximate online optimal—0
Efficient Model-Free Reinforcement Learning Using Gaussian Process—0
QMP: Q-switch Mixture of Policies for Multi-Task Behavior Sharing—0
Efficient Navigation of Colloidal Robots in an Unknown Environment via Deep Reinforcement Learning—0
BNAS:An Efficient Neural Architecture Search Approach Using Broad Scalable Architecture—0
Efficient Neural Clause-Selection Reinforcement—0
Efficient Off-Policy Safe Reinforcement Learning Using Trust Region Conditional Value at Risk—0
Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only—0
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
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Benchmark Results

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