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 2431–2440 of 15113 papers

TitleStatusHype
Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF—0
RLEEGNet: Integrating Brain-Computer Interfaces with Adaptive AI for Intuitive Responsiveness and High-Accuracy Motor Imagery Classification—0
Entropy-Regularized Token-Level Policy Optimization for Language Agent ReinforcementCode1
Learn to Teach: Sample-Efficient Privileged Learning for Humanoid Locomotion over Diverse Terrains—0
Monitored Markov Decision ProcessesCode0
High-Precision Geosteering via Reinforcement Learning and Particle Filters—0
Deceptive Path Planning via Reinforcement Learning with Graph Neural NetworksCode1
Value function interference and greedy action selection in value-based multi-objective reinforcement learning—0
ACTER: Diverse and Actionable Counterfactual Sequences for Explaining and Diagnosing RL Policies—0
Scaling Intelligent Agents in Combat Simulations for Wargaming—0
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Benchmark Results

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