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 3381–3390 of 15113 papers

TitleStatusHype
Process Supervision-Guided Policy Optimization for Code Generation—0
Primal-Dual Spectral Representation for Off-policy Evaluation—0
Dynamic Spectrum Access for Ambient Backscatter Communication-assisted D2D Systems with Quantum Reinforcement Learning—0
Optimizing Load Scheduling in Power Grids Using Reinforcement Learning and Markov Decision Processes—0
Learning Versatile Skills with Curriculum MaskingCode0
The Hive Mind is a Single Reinforcement Learning Agent—0
DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning—0
Survival of the Fittest: Evolutionary Adaptation of Policies for Environmental Shifts—0
Exploring RL-based LLM Training for Formal Language Tasks with Programmed RewardsCode0
Episodic Future Thinking Mechanism for Multi-agent Reinforcement Learning—0
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Benchmark Results

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