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 10376–10400 of 15113 papers

TitleStatusHype
Contextual Bandits for adapting to changing User preferences over time—0
Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion—0
DISPATCH: Design Space Exploration of Cyber-Physical Systems—0
Learn to Exceed: Stereo Inverse Reinforcement Learning with Concurrent Policy Optimization—0
Regret Bounds and Reinforcement Learning Exploration of EXP-based Algorithms—0
Lyapunov-Based Reinforcement Learning for Decentralized Multi-Agent Control—0
Multiplayer Support for the Arcade Learning Environment—0
Construction of Polar Codes with Reinforcement Learning—0
A Contraction Approach to Model-based Reinforcement Learning—0
GRAC: Self-Guided and Self-Regularized Actor-CriticCode0
HTMRL: Biologically Plausible Reinforcement Learning with Hierarchical Temporal MemoryCode0
Deep Reinforcement Learning for Closed-Loop Blood Glucose Control—0
Private Reinforcement Learning with PAC and Regret Guarantees—0
Efficient Reinforcement Learning Development with RLzoo—0
Reinforcement Learning for Weakly Supervised Temporal Grounding of Natural Language in Untrimmed Videos—0
Reconstructing Actions To Explain Deep Reinforcement Learning—0
Reward Maximisation through Discrete Active Inference—0
Knowledge-Assisted Deep Reinforcement Learning in 5G Scheduler Design: From Theoretical Framework to Implementation—0
GeneraLight: Improving Environment Generalization of Traffic Signal Control via Meta Reinforcement Learning—0
DRL-FAS: A Novel Framework Based on Deep Reinforcement Learning for Face Anti-Spoofing—0
Theory of Mind with Guilt Aversion Facilitates Cooperative Reinforcement Learning—0
Time your hedge with Deep Reinforcement Learning—0
Transfer Learning in Deep Reinforcement Learning: A Survey—0
Reinforcement Learning for Strategic Recommendations—0
Soft policy optimization using dual-track advantage estimator—0
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Benchmark Results

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