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 8651–8700 of 15113 papers

TitleStatusHype
Structured World Belief for Reinforcement Learning in POMDP—0
Multimodal Reward Shaping for Efficient Exploration in Reinforcement Learning—0
Provably Efficient Multi-Task Reinforcement Learning with Model Transfer—0
A New Representation of Successor Features for Transfer across Dissimilar Environments—0
Hierarchical Reinforcement Learning with Optimal Level Synchronization based on a Deep Generative ModelCode0
High-Accuracy Model-Based Reinforcement Learning, a Survey—0
On the Robustness of Deep Reinforcement Learning in IRS-Aided Wireless Communications Systems—0
Robust Risk-Sensitive Reinforcement Learning Agents for Trading Markets—0
MODRL/D-EL: Multiobjective Deep Reinforcement Learning with Evolutionary Learning for Multiobjective Optimization—0
Learning Heuristics for Template-based CEGIS of Loop Invariants with Reinforcement Learning—0
Geometric Value Iteration: Dynamic Error-Aware KL Regularization for Reinforcement Learning—0
Boosting the Convergence of Reinforcement Learning-based Auto-pruning Using Historical Data—0
Decentralized Multi-Agent Reinforcement Learning for Task Offloading Under Uncertainty—0
High-level Decisions from a Safe Maneuver Catalog with Reinforcement Learning for Safe and Cooperative Automated Merging—0
Deep Reinforcement Learning based Dynamic Optimization of Bus Timetable—0
Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi—0
PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided ExplorationCode0
NeuSaver: Neural Adaptive Power Consumption Optimization for Mobile Video Streaming—0
MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning—0
Reinforcement Learning for Education: Opportunities and Challenges—0
Minimizing Safety Interference for Safe and Comfortable Automated Driving with Distributional Reinforcement Learning—0
Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot—0
Safer Reinforcement Learning through Transferable Instinct NetworksCode0
Plan-Based Relaxed Reward Shaping for Goal-Directed Tasks—0
QoS-Aware Scheduling in New Radio Using Deep Reinforcement Learning—0
Model-free Reinforcement Learning for Robust Locomotion using Demonstrations from Trajectory Optimization—0
Mixing Human Demonstrations with Self-Exploration in Experience Replay for Deep Reinforcement Learning—0
Centralized Model and Exploration Policy for Multi-Agent RLCode0
Experimental Evidence that Empowerment May Drive Exploration in Sparse-Reward Environments—0
Deep Adaptive Multi-Intention Inverse Reinforcement LearningCode0
Going Beyond Linear RL: Sample Efficient Neural Function Approximation—0
A Deep Reinforcement Learning Approach for Traffic Signal Control Optimization—0
Cautious Policy Programming: Exploiting KL Regularization in Monotonic Policy Improvement for Reinforcement Learning—0
Carle's Game: An Open-Ended Challenge in Exploratory Machine CreativityCode0
Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage—0
Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability—0
Model Selection for Generic Reinforcement Learning—0
Modeling Explicit Concerning States for Reinforcement Learning in Visual DialogueCode0
The Role of Pretrained Representations for the OOD Generalization of Reinforcement Learning Agents—0
R3L: Connecting Deep Reinforcement Learning to Recurrent Neural Networks for Image Denoising via Residual Recovery—0
Reinforcement Learning based Proactive Control for Transmission Grid Resilience to Wildfire—0
Polynomial Time Reinforcement Learning in Factored State MDPs with Linear Value Functions—0
A Simple Reward-free Approach to Constrained Reinforcement Learning—0
CoBERL: Contrastive BERT for Reinforcement Learning—0
Behavior Constraining in Weight Space for Offline Reinforcement Learning—0
Generating stable molecules using imitation and reinforcement learning—0
Distributed Deep Reinforcement Learning for Intelligent Traffic Monitoring with a Team of Aerial Robots—0
LS3: Latent Space Safe Sets for Long-Horizon Visuomotor Control of Sparse Reward Iterative TasksCode0
NVCell: Standard Cell Layout in Advanced Technology Nodes with Reinforcement Learning—0
Safe Exploration by Solving Early Terminated MDP—0
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Benchmark Results

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