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 10751–10775 of 15113 papers

TitleStatusHype
Control-Aware Representations for Model-based Reinforcement Learning—0
Deep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environments—0
Batch-Constrained Reinforcement Learning for Dynamic Distribution Network Reconfiguration—0
Environment Shaping in Reinforcement Learning using State Abstraction—0
Risk-Sensitive Reinforcement Learning: a Martingale Approach to Reward Uncertainty—0
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping—0
Online Multi-agent Reinforcement Learning for Decentralized Inverter-based Volt-VAR Control—0
The Effect of Multi-step Methods on Overestimation in Deep Reinforcement Learning—0
Show me the Way: Intrinsic Motivation from Demonstrations—0
On the Relationship Between Active Inference and Control as Inference—0
Near-Optimal Reinforcement Learning with Self-Play—0
Provably Efficient Causal Reinforcement Learning with Confounded Observational Data—0
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs—0
QTRAN++: Improved Value Transformation for Cooperative Multi-Agent Reinforcement Learning—0
Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret—0
Constrained Combinatorial Optimization with Reinforcement Learning—0
Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning with Application to Autonomous Driving—0
dm_control: Software and Tasks for Continuous Control—0
Ecological Reinforcement Learning—0
Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage Control—0
Automated Optical Multi-layer Design via Deep Reinforcement LearningCode0
Reinforcement Learning for Mean Field Games with Strategic Complementarities—0
Gradient-EM Bayesian Meta-learning—0
Hierarchical Reinforcement Learning for Deep Goal Reasoning: An Expressiveness Analysis—0
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning—0
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Benchmark Results

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