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 3976–4000 of 15113 papers

TitleStatusHype
A reinforcement learning path planning approach for range-only underwater target localization with autonomous vehiclesCode1
DQNAS: Neural Architecture Search using Reinforcement Learning—0
Learning to solve arithmetic problems with a virtual abacus—0
Heterogeneous Multi-Robot Reinforcement LearningCode2
Adversarial Robust Deep Reinforcement Learning Requires Redefining Robustness—0
Sim-Anchored Learning for On-the-Fly AdaptationCode0
Show me what you want: Inverse reinforcement learning to automatically design robot swarms by demonstration—0
Neuro-Symbolic World Models for Adapting to Open World Novelty—0
Neuro-symbolic Meta Reinforcement Learning for Trading—0
CogReact: A Reinforced Framework to Model Human Cognitive Reaction Modulated by Dynamic Intervention—0
Reinforcement Learning for Protocol Synthesis in Resource-Constrained Wireless Sensor and IoT Networks—0
PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets—0
Risk-Averse Reinforcement Learning via Dynamic Time-Consistent Risk Measures—0
Deep-Reinforcement-Learning-based Path Planning for Industrial Robots using Distance Sensors as ObservationCode1
First Three Years of the International Verification of Neural Networks Competition (VNN-COMP)—0
Decentralized model-free reinforcement learning in stochastic games with average-reward objective—0
Hierarchical Deep Q-Learning Based Handover in Wireless Networks with Dual Connectivity—0
A Constrained-Optimization Approach to the Execution of Prioritized Stacks of Learned Multi-Robot Tasks—0
Multi-Target Landmark Detection with Incomplete Images via Reinforcement Learning and Shape Prior—0
Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning—0
Mutation Testing of Deep Reinforcement Learning Based on Real FaultsCode0
Safe Policy Improvement for POMDPs via Finite-State Controllers—0
Reinforcement Learning-based Joint Handover and Beam Tracking in Millimeter-wave Networks—0
Predictive World Models from Real-World Partial ObservationsCode0
Asynchronous training of quantum reinforcement learning—0
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Benchmark Results

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