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 151–160 of 15113 papers

TitleStatusHype
Thinking vs. Doing: Agents that Reason by Scaling Test-Time InteractionCode2
Learning to Clarify by Reinforcement Learning Through Reward-Weighted Fine-Tuning—0
Reliable Critics: Monotonic Improvement and Convergence Guarantees for Reinforcement Learning—0
Safety-Aware Reinforcement Learning for Control via Risk-Sensitive Action-Value Iteration and Quantile Regression—0
CARoL: Context-aware Adaptation for Robot Learning—0
On the Generalization of Data-Assisted Control in port-Hamiltonian Systems (DAC-pH)—0
QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine—0
Prompting Wireless Networks: Reinforced In-Context Learning for Power Control—0
Gradual Transition from Bellman Optimality Operator to Bellman Operator in Online Reinforcement LearningCode0
Towards Infant Sleep-Optimized Driving: Synergizing Wearable and Vehicle Sensing in Intelligent Cruise Control—0
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Benchmark Results

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