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 2581–2590 of 15113 papers

TitleStatusHype
Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes—0
ShiQ: Bringing back Bellman to LLMs—0
Certifying Stability of Reinforcement Learning Policies using Generalized Lyapunov Functions—0
Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLMCode0
Reinforcement Learning for AMR Charging Decisions: The Impact of Reward and Action Space Design—0
Learning When to Think: Shaping Adaptive Reasoning in R1-Style Models via Multi-Stage RLCode0
Developing and Integrating Trust Modeling into Multi-Objective Reinforcement Learning for Intelligent Agricultural Management—0
Attention-Based Reward Shaping for Sparse and Delayed RewardsCode0
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation—0
Fine-tuning Diffusion Policies with Backpropagation Through Diffusion Timesteps—0
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Benchmark Results

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