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 3901–3925 of 15113 papers

TitleStatusHype
Planning Multiple Epidemic Interventions with Reinforcement LearningCode0
Improved Regret for Efficient Online Reinforcement Learning with Linear Function Approximation—0
Identifying Expert Behavior in Offline Training Datasets Improves Behavioral Cloning of Robotic Manipulation PoliciesCode0
STEEL: Singularity-aware Reinforcement Learning—0
Transferring Multiple Policies to Hotstart Reinforcement Learning in an Air Compressor Management Problem—0
Regret Bounds for Markov Decision Processes with Recursive Optimized Certainty Equivalents—0
Guiding Online Reinforcement Learning with Action-Free Offline PretrainingCode1
Hierarchical Programmatic Reinforcement Learning via Learning to Compose Programs—0
Autonomous Satellite Docking via Adaptive Optimal Output Rregulation: A Reinforcement Learning Approach—0
A Deep Reinforcement Learning Framework for Optimizing Congestion Control in Data Centers—0
Sample Efficient Deep Reinforcement Learning via Local Planning—0
Turbulence control in plane Couette flow using low-dimensional neural ODE-based models and deep reinforcement learning—0
Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World ModellingCode1
SaFormer: A Conditional Sequence Modeling Approach to Offline Safe Reinforcement Learning—0
Constrained Policy Optimization with Explicit Behavior Density for Offline Reinforcement LearningCode0
Beyond Exponentially Fast Mixing in Average-Reward Reinforcement Learning via Multi-Level Monte Carlo Actor-Critic—0
Towards Learning Rubik's Cube with N-tuple-based Reinforcement Learning—0
STEERING: Stein Information Directed Exploration for Model-Based Reinforcement Learning—0
A Memory Efficient Deep Reinforcement Learning Approach For Snake Game Autonomous Agents—0
Generalized Munchausen Reinforcement Learning using Tsallis KL Divergence—0
Outcome-directed Reinforcement Learning by Uncertainty & Temporal Distance-Aware Curriculum Goal GenerationCode1
Single-Trajectory Distributionally Robust Reinforcement Learning—0
Reinforcement Learning from Diverse Human Preferences—0
Modeling human road crossing decisions as reward maximization with visual perception limitations—0
Solving Richly Constrained Reinforcement Learning through State Augmentation and Reward Penalties—0
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Benchmark Results

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