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 7101–7125 of 15113 papers

TitleStatusHype
Evolutionary reinforcement learning of dynamical large deviations—0
Evolutionary Reinforcement Learning via Cooperative Coevolutionary Negatively Correlated Search—0
Evolutionary RL for Container Loading—0
Evolution of cooperation in the public goods game with Q-learning—0
Evolution Strategies as an Alternate Learning method for Hierarchical Reinforcement Learning—0
Evolution Strategies Converges to Finite Differences—0
Scalable Reinforcement Post-Training Beyond Static Human Prompts: Evolving Alignment via Asymmetric Self-Play—0
Evolving Curricula with Regret-Based Environment Design—0
Evolving Hierarchical Memory-Prediction Machines in Multi-Task Reinforcement Learning—0
Evolving Neural Networks in Reinforcement Learning by means of UMDAc—0
Evolving Populations of Diverse RL Agents with MAP-Elites—0
Evolving Reinforcement Learning Environment to Minimize Learner's Achievable Reward: An Application on Hardening Active Directory Systems—0
Evolving Rewards to Automate Reinforcement Learning—0
Evolving-to-Learn Reinforcement Learning Tasks with Spiking Neural Networks—0
Evo-NAS: Evolutionary-Neural Hybrid Agent for Architecture Search—0
EVO-RL: Evolutionary-Driven Reinforcement Learning—0
Exact Reduction of Huge Action Spaces in General Reinforcement Learning—0
Examining average and discounted reward optimality criteria in reinforcement learning—0
Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks—0
Exchangeable Input Representations for Reinforcement Learning—0
Excluding the Irrelevant: Focusing Reinforcement Learning through Continuous Action Masking—0
Exclusively Penalized Q-learning for Offline Reinforcement Learning—0
Execute Order 66: Targeted Data Poisoning for Reinforcement Learning—0
ExpanRL: Hierarchical Reinforcement Learning for Course Concept Expansion in MOOCs—0
Expected Policy Gradients for Reinforcement Learning—0
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Benchmark Results

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