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 3891–3900 of 15113 papers

TitleStatusHype
Decision Mamba: Reinforcement Learning via Hybrid Selective Sequence Modeling—0
Hybrid Reinforcement Learning Framework for Mixed-Variable Problems—0
Learning to Discuss Strategically: A Case Study on One Night Ultimate Werewolf—0
Efficient Stimuli Generation using Reinforcement Learning in Design Verification—0
SleeperNets: Universal Backdoor Poisoning Attacks Against Reinforcement Learning Agents—0
From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems—0
Bilevel reinforcement learning via the development of hyper-gradient without lower-level convexity—0
Kernel Metric Learning for In-Sample Off-Policy Evaluation of Deterministic RL PoliciesCode0
Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF—0
Preferred-Action-Optimized Diffusion Policies for Offline Reinforcement Learning—0
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Benchmark Results

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