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 6576–6600 of 15113 papers

TitleStatusHype
The Sample Complexity of Teaching-by-Reinforcement on Q-Learning—0
The tree reconstruction game: phylogenetic reconstruction using reinforcement learning—0
The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task—0
The Utility of Sparse Representations for Control in Reinforcement Learning—0
The Value Equivalence Principle for Model-Based Reinforcement Learning—0
The Value Function Polytope in Reinforcement Learning—0
The Value-Improvement Path: Towards Better Representations for Reinforcement Learning—0
The Value of Reward Lookahead in Reinforcement Learning—0
The Virtues of Pessimism in Inverse Reinforcement Learning—0
The wisdom of the crowd: reliable deep reinforcement learning through ensembles of Q-functions—0
Think2SQL: Reinforce LLM Reasoning Capabilities for Text2SQL—0
Thinking While Moving: Deep Reinforcement Learning with Concurrent Control—0
Think Silently, Think Fast: Dynamic Latent Compression of LLM Reasoning Chains—0
Think Twice: Enhancing LLM Reasoning by Scaling Multi-round Test-time Thinking—0
Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints—0
Thompson Sampling for Learning Parameterized Markov Decision Processes—0
Thompson Sampling is Asymptotically Optimal in General Environments—0
Thompson Sampling on Asymmetric α-Stable Bandits—0
Thompson Sampling with a Mixture Prior—0
Thought-Augmented Policy Optimization: Bridging External Guidance and Internal Capabilities—0
Throughput Optimization for Grant-Free Multiple Access With Multiagent Deep Reinforcement Learning—0
Through the Valley: Path to Effective Long CoT Training for Small Language Models—0
Tight Bayesian Ambiguity Sets for Robust MDPs—0
Tightening Exploration in Upper Confidence Reinforcement Learning—0
Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds—0
Show:102550
← PrevPage 264 of 605Next →

Benchmark Results

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