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 1871–1880 of 15113 papers

TitleStatusHype
Diffusion Spectral Representation for Reinforcement Learning—0
Decentralized Transformers with Centralized Aggregation are Sample-Efficient Multi-Agent World ModelsCode0
Multistep Criticality Search and Power Shaping in Microreactors with Reinforcement Learning—0
Open Problem: Order Optimal Regret Bounds for Kernel-Based Reinforcement Learning—0
SiT: Symmetry-Invariant Transformers for Generalisation in Reinforcement LearningCode0
KalMamba: Towards Efficient Probabilistic State Space Models for RL under Uncertainty—0
Direct Multi-Turn Preference Optimization for Language AgentsCode2
MacroHFT: Memory Augmented Context-aware Reinforcement Learning On High Frequency TradingCode2
RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-FoldCode1
Beyond Optimism: Exploration With Partially Observable RewardsCode0
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Benchmark Results

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