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 2871–2880 of 15113 papers

TitleStatusHype
A Framework for Empowering Reinforcement Learning Agents with Causal Analysis: Enhancing Automated Cryptocurrency Trading—0
LgTS: Dynamic Task Sampling using LLM-generated sub-goals for Reinforcement Learning Agents—0
Reduced Policy Optimization for Continuous Control with Hard ConstraintsCode1
Hybrid Reinforcement Learning for Optimizing Pump Sustainability in Real-World Water Distribution Networks—0
Exploration with Principles for Diverse AI Supervision—0
Leveraging Optimal Transport for Enhanced Offline Reinforcement Learning in Surgical Robotic Environments—0
METRA: Scalable Unsupervised RL with Metric-Aware AbstractionCode1
Learning RL-Policies for Joint Beamforming Without Exploration: A Batch Constrained Off-Policy ApproachCode0
Dealing with uncertainty: balancing exploration and exploitation in deep recurrent reinforcement learningCode0
Discerning Temporal Difference Learning—0
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Benchmark Results

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