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 1281–1290 of 15113 papers

TitleStatusHype
AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with TransformersCode2
Modulating Reservoir Dynamics via Reinforcement Learning for Efficient Robot Skill Synthesis—0
Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents—0
An Investigation of Offline Reinforcement Learning in Factorisable Action SpacesCode0
Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment—0
Stable Continual Reinforcement Learning via Diffusion-based Trajectory Replay—0
Multi-agent Path Finding for Timed Tasks using Evolutionary Games—0
Continual Adversarial Reinforcement Learning (CARL) of False Data Injection detection: forgetting and explainability—0
The Surprising Ineffectiveness of Pre-Trained Visual Representations for Model-Based Reinforcement Learning—0
Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review—0
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Benchmark Results

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