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 3931–3940 of 15113 papers

TitleStatusHype
Certifiably Robust Reinforcement Learning through Model-Based Abstract Interpretation—0
Learning to Generate All Feasible Actions—0
Model-based Offline Reinforcement Learning with Local Misspecification—0
FedHQL: Federated Heterogeneous Q-Learning—0
Trust Region-Based Safe Distributional Reinforcement Learning for Multiple ConstraintsCode1
Automatic Intrinsic Reward Shaping for Exploration in Deep Reinforcement LearningCode3
Deep Laplacian-based Options for Temporally-Extended ExplorationCode1
Which Experiences Are Influential for Your Agent? Policy Iteration with Turn-over DropoutCode0
Learning from Multiple Independent Advisors in Multi-agent Reinforcement LearningCode0
Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement LearningCode0
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Benchmark Results

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