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 8651–8675 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
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Benchmark Results

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