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 9201–9225 of 15113 papers

TitleStatusHype
Provable Multi-Objective Reinforcement Learning with Generative Models—0
Provable Partially Observable Reinforcement Learning with Privileged Information—0
Provable Reinforcement Learning with a Short-Term Memory—0
Provable Reset-free Reinforcement Learning by No-Regret Reduction—0
Provable Rich Observation Reinforcement Learning with Combinatorial Latent States—0
Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation—0
Provable RL with Exogenous Distractors via Multistep Inverse Dynamics—0
Provable Self-Play Algorithms for Competitive Reinforcement Learning—0
Provable Traffic Rule Compliance in Safe Reinforcement Learning on the Open Sea—0
Provably Adaptive Average Reward Reinforcement Learning for Metric Spaces—0
Provably adaptive reinforcement learning in metric spaces—0
Provably Correct Automata Embeddings for Optimal Automata-Conditioned Reinforcement Learning—0
Provably Efficient Action-Manipulation Attack Against Continuous Reinforcement Learning—0
Provably Efficient Algorithm for Nonstationary Low-Rank MDPs—0
Provably Efficient Algorithms for Multi-Objective Competitive RL—0
Provably Efficient Black-Box Action Poisoning Attacks Against Reinforcement Learning—0
Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization—0
Provably Efficient Causal Reinforcement Learning with Confounded Observational Data—0
Provably Efficient Convergence of Primal-Dual Actor-Critic with Nonlinear Function Approximation—0
Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximation—0
Provably Efficient CVaR RL in Low-rank MDPs—0
Provably Efficient Exploration in Policy Optimization—0
Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret—0
Provably Efficient Exploration in Reward Machines with Low Regret—0
Provably Efficient Iterated CVaR Reinforcement Learning with Function Approximation and Human Feedback—0
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Benchmark Results

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