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 776–800 of 15113 papers

TitleStatusHype
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement LearningCode1
DEIR: Efficient and Robust Exploration through Discriminative-Model-Based Episodic Intrinsic RewardsCode1
Sample-efficient Model-based Reinforcement Learning for Quantum ControlCode1
Bridging RL Theory and Practice with the Effective HorizonCode1
Benchmarking Actor-Critic Deep Reinforcement Learning Algorithms for Robotics Control with Action ConstraintsCode1
Language Instructed Reinforcement Learning for Human-AI CoordinationCode1
Eagle: End-to-end Deep Reinforcement Learning based Autonomous Control of PTZ CamerasCode1
RESPECT: Reinforcement Learning based Edge Scheduling on Pipelined Coral Edge TPUsCode1
Stable and Safe Reinforcement Learning via a Barrier-Lyapunov Actor-Critic ApproachCode1
Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agentsCode1
Optimal Goal-Reaching Reinforcement Learning via Quasimetric LearningCode1
Multi-view Tensor Graph Neural Networks Through Reinforced AggregationCode1
Offline RL with No OOD Actions: In-Sample Learning via Implicit Value RegularizationCode1
Inverse Reinforcement Learning without Reinforcement LearningCode1
Optimal Transport for Offline Imitation LearningCode1
marl-jax: Multi-Agent Reinforcement Leaning FrameworkCode1
DataLight: Offline Data-Driven Traffic Signal ControlCode1
Imitating Graph-Based Planning with Goal-Conditioned PoliciesCode1
Reinforcement Learning Friendly Vision-Language Model for MinecraftCode1
Transformer-based World Models Are Happy With 100k InteractionsCode1
Synthetic Experience ReplayCode1
User Retention-oriented Recommendation with Decision TransformerCode1
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-TuningCode1
Diminishing Return of Value Expansion Methods in Model-Based Reinforcement LearningCode1
A Multiplicative Value Function for Safe and Efficient Reinforcement LearningCode1
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Benchmark Results

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