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

Showing 59015950 of 15113 papers

TitleStatusHype
Offline Reinforcement Learning from Human Feedback in Real-World Sequence-to-Sequence Tasks0
Learning from Longitudinal Face Demonstration - Where Tractable Deep Modeling Meets Inverse Reinforcement Learning0
Learning from Outside the Viability Kernel: Why we Should Build Robots that can Fall with Grace0
Learning from Peers: Deep Transfer Reinforcement Learning for Joint Radio and Cache Resource Allocation in 5G RAN Slicing0
Learning from Reward-Free Offline Data: A Case for Planning with Latent Dynamics Models0
Learning from Simulation, Racing in Reality0
Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network0
Learning from Symmetry: Meta-Reinforcement Learning with Symmetrical Behaviors and Language Instructions0
Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate0
Learning Functionally Decomposed Hierarchies for Continuous Navigation Tasks0
Learning Future Representation with Synthetic Observations for Sample-efficient Reinforcement Learning0
Learning Gaussian Policies from Smoothed Action Value Functions0
Learning Generalizable Agents via Saliency-Guided Features Decorrelation0
Learning Generalized Wireless MAC Communication Protocols via Abstraction0
Learning General-Purpose Controllers via Locally Communicating Sensorimotor Modules0
Learning General World Models in a Handful of Reward-Free Deployments0
Learning Goal-Conditioned Value Functions with one-step Path rewards rather than Goal-Rewards0
Learning Goal-Oriented Visual Dialog Agents: Imitating and Surpassing Analytic Experts0
Learning Good Policies By Learning Good Perceptual Models0
Learning Good Representation via Continuous Attention0
LearningGroup: A Real-Time Sparse Training on FPGA via Learnable Weight Grouping for Multi-Agent Reinforcement Learning0
Learning Heuristics for Automated Reasoning through Reinforcement Learning0
Learning Heuristics for Quantified Boolean Formulas through Reinforcement Learning0
Learning Hierarchical Structures On-The-Fly with a Recurrent-Recursive Model for Sequences0
Learning how to Interact with a Complex Interface using Hierarchical Reinforcement Learning0
Learning how to learn: an adaptive dialogue agent for incrementally learning visually grounded word meanings0
Learning How to Self-Learn: Enhancing Self-Training Using Neural Reinforcement Learning0
Learning Human Rewards by Inferring Their Latent Intelligence Levels in Multi-Agent Games: A Theory-of-Mind Approach with Application to Driving Data0
Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation0
Learning impartial policies for sequential counterfactual explanations using Deep Reinforcement Learning0
Learning in Factored Domains with Information-Constrained Visual Representations0
Learning in games via reinforcement and regularization0
Learning In-Hand Translation Using Tactile Skin With Shear and Normal Force Sensing0
Learning in Markov Decision Processes under Constraints0
Learning in Observable POMDPs, without Computationally Intractable Oracles0
Learning in Sparse Rewards settings through Quality-Diversity algorithms0
Learning Interaction-aware Guidance Policies for Motion Planning in Dense Traffic Scenarios0
Learning Interpretable Models of Aircraft Handling Behaviour by Reinforcement Learning from Human Feedback0
Learning Intrinsically Motivated Options to Stimulate Policy Exploration0
Learning Intrinsic Symbolic Rewards in Reinforcement Learning0
Learning Invariable Semantical Representation from Language for Extensible Policy Generalization0
Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning0
Learning Invariant Reward Functions through Trajectory Interventions0
Learning Key Steps to Attack Deep Reinforcement Learning Agents0
Learning Latent Landmarks for Generalizable Planning0
Learning Latent State Spaces for Planning through Reward Prediction0
Learning List-wise Representation in Reinforcement Learning for Ads Allocation with Multiple Auxiliary Tasks0
Learning Locomotion Skills Using DeepRL: Does the Choice of Action Space Matter?0
Learning Lower Bounds for Graph Exploration With Reinforcement Learning0
Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning0
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Benchmark Results

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