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 3481–3490 of 15113 papers

TitleStatusHype
Cross-Embodiment Dexterous Grasping with Reinforcement Learning—0
Dual Active Learning for Reinforcement Learning from Human Feedback—0
End-to-end Driving in High-Interaction Traffic Scenarios with Reinforcement Learning—0
Solving Reach-Avoid-Stay Problems Using Deep Deterministic Policy Gradients—0
Beyond Expected Returns: A Policy Gradient Algorithm for Cumulative Prospect Theoretic Reinforcement Learning—0
Efficient Residual Learning with Mixture-of-Experts for Universal Dexterous Grasping—0
Adaptive teachers for amortized samplersCode0
PreND: Enhancing Intrinsic Motivation in Reinforcement Learning through Pre-trained Network Distillation—0
Sampling from Energy-based Policies using Diffusion—0
LLM-Augmented Symbolic Reinforcement Learning with Landmark-Based Task Decomposition—0
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Benchmark Results

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