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 5326–5350 of 15113 papers

TitleStatusHype
Rewardless Open-Ended Learning (ROEL)—0
Reward Machine Inference for Robotic Manipulation—0
Reward (Mis)design for Autonomous Driving—0
Reward Poisoning Attacks on Offline Multi-Agent Reinforcement Learning—0
Reward Poisoning in Reinforcement Learning: Attacks Against Unknown Learners in Unknown Environments—0
Reward prediction for representation learning and reward shaping—0
Reward-Predictive Clustering—0
STIR^2: Reward Relabelling for combined Reinforcement and Imitation Learning on sparse-reward tasks—0
Reward-Respecting Subtasks for Model-Based Reinforcement Learning—0
Rewards Encoding Environment Dynamics Improves Preference-based Reinforcement Learning—0
Reward Shaping for Reinforcement Learning with Omega-Regular Objectives—0
Reward Shaping for User Satisfaction in a REINFORCE Recommender—0
Reward Shaping via Diffusion Process in Reinforcement Learning—0
Reward Shaping via Meta-Learning—0
Reward Shaping with Dynamic Trajectory Aggregation—0
Reward Shaping with Subgoals for Social Navigation—0
RewardsOfSum: Exploring Reinforcement Learning Rewards for Summarisation—0
Rewards with Negative Examples for Reinforced Topic-Focused Abstractive Summarization—0
Reward Tampering Problems and Solutions in Reinforcement Learning: A Causal Influence Diagram Perspective—0
Reward Training Wheels: Adaptive Auxiliary Rewards for Robotics Reinforcement Learning—0
REX: Rapid Exploration and eXploitation for AI Agents—0
ReZero: Enhancing LLM search ability by trying one-more-time—0
RIDM: Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstration—0
Riemannian Stochastic Gradient Method for Nested Composition Optimization—0
RILe: Reinforced Imitation Learning—0
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Benchmark Results

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