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 3981–3990 of 15113 papers

TitleStatusHype
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
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Benchmark Results

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