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 1926–1950 of 15113 papers

TitleStatusHype
Backprop-Free Reinforcement Learning with Active Neural Generative CodingCode1
Combinatorial Optimization with Policy Adaptation using Latent Space SearchCode1
Bridging the Gap Between f-GANs and Wasserstein GANsCode1
Safe Navigation: Training Autonomous Vehicles using Deep Reinforcement Learning in CARLACode1
Safe Offline Reinforcement Learning with Real-Time Budget ConstraintsCode1
Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement LearningCode1
Safe Reinforcement Learning in Constrained Markov Decision ProcessesCode1
Bridging State and History Representations: Understanding Self-Predictive RLCode1
Safe Reinforcement Learning via Curriculum InductionCode1
SAMBA: Safe Model-Based & Active Reinforcement LearningCode1
Same State, Different Task: Continual Reinforcement Learning without InterferenceCode1
Sample-Efficient Automated Deep Reinforcement LearningCode1
Sample-efficient Cross-Entropy Method for Real-time PlanningCode1
Sample-efficient Model-based Reinforcement Learning for Quantum ControlCode1
Sample Efficient Reinforcement Learning through Learning from Demonstrations in MinecraftCode1
Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and ExploitationCode1
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement LearningCode1
SARL*: Deep Reinforcement Learning based Human-Aware Navigation for Mobile Robot in Indoor EnvironmentsCode1
SATORI-R1: Incentivizing Multimodal Reasoning with Spatial Grounding and Verifiable RewardsCode1
Scalable Bayesian Inverse Reinforcement LearningCode1
Scalable Multi-Agent Model-Based Reinforcement LearningCode1
A multi-agent reinforcement learning model of common-pool resource appropriationCode1
Scalable Planning and Learning for Multiagent POMDPs: Extended VersionCode1
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximationCode1
Combining Deep Reinforcement Learning and Search for Imperfect-Information GamesCode1
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Benchmark Results

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