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 3491–3500 of 15113 papers

TitleStatusHype
Don't flatten, tokenize! Unlocking the key to SoftMoE's efficacy in deep RL—0
LLM-Augmented Symbolic Reinforcement Learning with Landmark-Based Task Decomposition—0
ComaDICE: Offline Cooperative Multi-Agent Reinforcement Learning with Stationary Distribution Shift Regularization—0
Sampling from Energy-based Policies using Diffusion—0
PreND: Enhancing Intrinsic Motivation in Reinforcement Learning through Pre-trained Network Distillation—0
Absolute State-wise Constrained Policy Optimization: High-Probability State-wise Constraints Satisfaction—0
Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Space—0
Upper and Lower Bounds for Distributionally Robust Off-Dynamics Reinforcement Learning—0
Task-agnostic Pre-training and Task-guided Fine-tuning for Versatile Diffusion Planner—0
Personalisation via Dynamic Policy Fusion—0
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Benchmark Results

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