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 2911–2920 of 15113 papers

TitleStatusHype
Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-Visual Environments: A Comparison—0
AURO: Reinforcement Learning for Adaptive User Retention Optimization in Recommender Systems—0
Optimal Control of District Cooling Energy Plant with Reinforcement Learning and MPC—0
LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option FrameworkCode0
RTDK-BO: High Dimensional Bayesian Optimization with Reinforced Transformer Deep kernels—0
Safe Exploration in Reinforcement Learning: A Generalized Formulation and Algorithms—0
Constraint-Conditioned Policy Optimization for Versatile Safe Reinforcement Learning—0
How the level sampling process impacts zero-shot generalisation in deep reinforcement learning—0
Resilient Legged Local Navigation: Learning to Traverse with Compromised Perception End-to-End—0
Discovering General Reinforcement Learning Algorithms with Adversarial Environment DesignCode1
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Benchmark Results

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