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 4621–4630 of 15113 papers

TitleStatusHype
Online RL in Linearly q^π-Realizable MDPs Is as Easy as in Linear MDPs If You Learn What to Ignore—0
Reinforcement Learning in a Safety-Embedded MDP with Trajectory Optimization—0
Spectral Entry-wise Matrix Estimation for Low-Rank Reinforcement Learning—0
Scalable Semantic Non-Markovian Simulation Proxy for Reinforcement Learning—0
Bi-Level Offline Policy Optimization with Limited Exploration—0
f-Policy Gradients: A General Framework for Goal Conditioned RL using f-Divergences—0
On Double Descent in Reinforcement Learning with LSTD and Random Features—0
When is Agnostic Reinforcement Learning Statistically Tractable?—0
Predictive auxiliary objectives in deep RL mimic learning in the brain—0
Multi-timestep models for Model-based Reinforcement Learning—0
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Benchmark Results

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