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 5876–5900 of 15113 papers

TitleStatusHype
Data-Driven Evaluation of Training Action Space for Reinforcement Learning—0
Grounding Hindsight Instructions in Multi-Goal Reinforcement Learning for RoboticsCode1
On Improving Cross-dataset Generalization of Deepfake Detectors—0
The Complexity of Markov Equilibrium in Stochastic Games—0
Semantic Exploration from Language Abstractions and Pretrained Representations—0
Q-learning with online random forests—0
Optimizing the Long-Term Behaviour of Deep Reinforcement Learning for Pushing and Grasping—0
Offline Reinforcement Learning for Safer Blood Glucose Control in People with Type 1 DiabetesCode1
Temporal Alignment for History Representation in Reinforcement LearningCode0
Habitat-Web: Learning Embodied Object-Search Strategies from Human Demonstrations at Scale—0
Imitating, Fast and Slow: Robust learning from demonstrations via decision-time planning—0
Distributed Reinforcement Learning for Robot Teams: A Review—0
Federated Reinforcement Learning with Environment HeterogeneityCode1
Standardized feature extraction from pairwise conflicts applied to the train rescheduling problem—0
On the Computational Consequences of Cost Function Design in Nonlinear Optimal Control—0
RL4ReAl: Reinforcement Learning for Register Allocation—0
Multi-Agent Distributed Reinforcement Learning for Making Decentralized Offloading DecisionsCode1
Learning to Bid Long-Term: Multi-Agent Reinforcement Learning with Long-Term and Sparse Reward in Repeated Auction GamesCode0
Inferring Rewards from Language in ContextCode1
Jump-Start Reinforcement LearningCode1
Configuration Path Control—0
Automating Reinforcement Learning with Example-based ResetsCode0
Reinforcement Learning Agents in Colonel BlottoCode0
Value Gradient weighted Model-Based Reinforcement LearningCode1
Optimising Energy Efficiency in UAV-Assisted Networks using Deep Reinforcement Learning—0
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Benchmark Results

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