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 3611–3620 of 15113 papers

TitleStatusHype
Real-time scheduling of renewable power systems through planning-based reinforcement learning—0
Conceptual Reinforcement Learning for Language-Conditioned Tasks—0
Computably Continuous Reinforcement-Learning Objectives are PAC-learnable—0
Task Aware Dreamer for Task Generalization in Reinforcement Learning—0
Exploiting Contextual Structure to Generate Useful Auxiliary Tasks—0
Beware of Instantaneous Dependence in Reinforcement Learning—0
Power and Interference Control for VLC-Based UDN: A Reinforcement Learning Approach—0
RACCER: Towards Reachable and Certain Counterfactual Explanations for Reinforcement LearningCode0
MCTS-GEB: Monte Carlo Tree Search is a Good E-graph BuilderCode0
Using Memory-Based Learning to Solve Tasks with State-Action Constraints—0
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Benchmark Results

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