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 4251–4260 of 15113 papers

TitleStatusHype
Monitored Markov Decision ProcessesCode0
Value function interference and greedy action selection in value-based multi-objective reinforcement learning—0
ACTER: Diverse and Actionable Counterfactual Sequences for Explaining and Diagnosing RL Policies—0
High-Precision Geosteering via Reinforcement Learning and Particle Filters—0
Learn to Teach: Sample-Efficient Privileged Learning for Humanoid Locomotion over Diverse Terrains—0
Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices—0
Differentially Private Deep Model-Based Reinforcement Learning—0
Multi-Timescale Ensemble Q-learning for Markov Decision Process Policy OptimizationCode0
Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RLCode0
Scaling Intelligent Agents in Combat Simulations for Wargaming—0
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Benchmark Results

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