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 2501–2525 of 15113 papers

TitleStatusHype
Augmenting Replay in World Models for Continual Reinforcement LearningCode0
Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control—0
M2CURL: Sample-Efficient Multimodal Reinforcement Learning via Self-Supervised Representation Learning for Robotic ManipulationCode1
Context-Former: Stitching via Latent Conditioned Sequence Modeling—0
SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning—0
The Indoor-Training Effect: unexpected gains from distribution shifts in the transition function—0
LEACH-RLC: Enhancing IoT Data Transmission with Optimized Clustering and Reinforcement LearningCode0
Social Interpretable Reinforcement Learning—0
Health Text Simplification: An Annotated Corpus for Digestive Cancer Education and Novel Strategies for Reinforcement LearningCode0
On the Limitations of Markovian Rewards to Express Multi-Objective, Risk-Sensitive, and Modal Tasks—0
Learning fast changing slow in spiking neural networks—0
Hierarchical Continual Reinforcement Learning via Large Language Model—0
Scilab-RL: A software framework for efficient reinforcement learning and cognitive modeling research—0
HMM for Discovering Decision-Making Dynamics Using Reinforcement Learning ExperimentsCode0
Sample Efficient Reinforcement Learning by Automatically Learning to Compose Subtasks—0
True Knowledge Comes from Practice: Aligning LLMs with Embodied Environments via Reinforcement LearningCode2
Learning-based sensing and computing decision for data freshness in edge computing-enabled networks—0
Constant Stepsize Q-learning: Distributional Convergence, Bias and Extrapolation—0
SEER: Facilitating Structured Reasoning and Explanation via Reinforcement LearningCode1
DittoGym: Learning to Control Soft Shape-Shifting RobotsCode1
Stable and Safe Human-aligned Reinforcement Learning through Neural Ordinary Differential EquationsCode1
A Safe Reinforcement Learning Algorithm for Supervisory Control of Power Plants—0
Towards Socially and Morally Aware RL agent: Reward Design With LLM—0
On the Stochastic (Variance-Reduced) Proximal Gradient Method for Regularized Expected Reward Optimization—0
Active Inference as a Model of Agency—0
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Benchmark Results

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