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 4041–4050 of 15113 papers

TitleStatusHype
Data-Incremental Continual Offline Reinforcement Learning—0
Reinforcement Learning Approach for Integrating Compressed Contexts into Knowledge Graphs—0
TrajDeleter: Enabling Trajectory Forgetting in Offline Reinforcement Learning AgentsCode0
Actor-Critic Reinforcement Learning with Phased Actor—0
LTL-Constrained Policy Optimization with Cycle Experience Replay—0
Learn to Tour: Operator Design For Solution Feasibility Mapping in Pickup-and-delivery Traveling Salesman Problem—0
Prompt Optimizer of Text-to-Image Diffusion Models for Abstract Concept Understanding—0
Physics-informed Actor-Critic for Coordination of Virtual Inertia from Power Distribution Systems—0
Achieving Constant Regret in Linear Markov Decision Processes—0
Simplex Decomposition for Portfolio Allocation Constraints in Reinforcement Learning—0
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Benchmark Results

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