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 3701–3710 of 15113 papers

TitleStatusHype
Optimality theory of stigmergic collective information processing by chemotactic cells—0
Rocket Landing Control with Random Annealing Jump Start Reinforcement Learning—0
Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RLCode0
Phase Re-service in Reinforcement Learning Traffic Signal Control—0
FuzzTheREST: An Intelligent Automated Black-box RESTful API Fuzzer—0
Track-MDP: Reinforcement Learning for Target Tracking with Controlled Sensing—0
Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving GeneralizationCode0
Reinforcement Learning: Tutorial and Survey—0
Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction—0
ROLeR: Effective Reward Shaping in Offline Reinforcement Learning for Recommender SystemsCode0
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Benchmark Results

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