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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2001–2010 of 15113 papers

TitleStatusHype
Learning to Discuss Strategically: A Case Study on One Night Ultimate Werewolf—0
Diffusion Policies creating a Trust Region for Offline Reinforcement LearningCode1
RLeXplore: Accelerating Research in Intrinsically-Motivated Reinforcement Learning—0
Safety through Permissibility: Shield Construction for Fast and Safe Reinforcement Learning—0
Policy Zooming: Adaptive Discretization-based Infinite-Horizon Average-Reward Reinforcement Learning—0
Kernel Metric Learning for In-Sample Off-Policy Evaluation of Deterministic RL PoliciesCode0
Preferred-Action-Optimized Diffusion Policies for Offline Reinforcement Learning—0
Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF—0
A Study of Plasticity Loss in On-Policy Deep Reinforcement LearningCode0
Imitating from auxiliary imperfect demonstrations via Adversarial Density Weighted RegressionCode0
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Benchmark Results

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