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 3941–3950 of 15113 papers

TitleStatusHype
Depending on yourself when you should: Mentoring LLM with RL agents to become the master in cybersecurity games—0
Batch-Augmented Multi-Agent Reinforcement Learning for Efficient Traffic Signal Optimization—0
Coordinating Ride-Pooling with Public Transit using Reward-Guided Conservative Q-Learning: An Offline Training and Online Fine-Tuning Reinforcement Learning Framework—0
Deploying Reinforcement Learning in Water Transport—0
Assessing the Zero-Shot Capabilities of LLMs for Action Evaluation in RL—0
Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel—0
Depth and nonlinearity induce implicit exploration for RL—0
Depth-Constrained ASV Navigation with Deep RL and Limited Sensing—0
Coordinating Disaster Emergency Response with Heuristic Reinforcement Learning—0
A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning—0
Show:102550
← PrevPage 395 of 1512Next →

Benchmark Results

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