← Methods
Reinforcement Learning › Methods ›

Deep RL

3861 directly classified papers

Papers per year

Papers

Efficient Reinforcement Learning by Discovering Neural Pathways NIPS 2024
Taming "data-hungry" reinforcement learning? Stability in continuous state-action spaces NIPS 2024
Disentangled Unsupervised Skill Discovery for Efficient Hierarchical Reinforcement Learning NIPS 2024
Conversational Question Answering with Language Models Generated Reformulations over Knowledge Graph ACL 2024
CrystalBox: Future-Based Explanations for Input-Driven Deep RL Systems AAAI 2024
Embodied Multi-Modal Agent trained by an LLM from a Parallel TextWorld CVPR 2024
OPPerTune: Post-Deployment Configuration Tuning of Services Made Easy NSDI 2024
A Transfer Approach Using Graph Neural Networks in Deep Reinforcement Learning AAAI 2024
AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers NIPS 2024
ConcaveQ: Non-monotonic Value Function Factorization via Concave Representations in Deep Multi-Agent Reinforcement Learning AAAI 2024
Cloud-LoRa: Enabling Cloud Radio Access LoRa Networks Using Reinforcement Learning Based Bandwidth-Adaptive Compression NSDI 2024
OCEAN-MBRL: Offline Conservative Exploration for Model-Based Offline Reinforcement Learning AAAI 2024
Exploring Gradient Explosion in Generative Adversarial Imitation Learning: A Probabilistic Perspective AAAI 2024
Simplifying Latent Dynamics with Softly State-Invariant World Models NIPS 2024
Generating Code World Models with Large Language Models Guided by Monte Carlo Tree Search NIPS 2024
RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement Learning NIPS 2024
Real-Time Recurrent Learning using Trace Units in Reinforcement Learning NIPS 2024
Learning World Models for Unconstrained Goal Navigation NIPS 2024
Exploring the Edges of Latent State Clusters for Goal-Conditioned Reinforcement Learning NIPS 2024
REBEL: Reinforcement Learning via Regressing Relative Rewards NIPS 2024
Pre-Trained Multi-Goal Transformers with Prompt Optimization for Efficient Online Adaptation NIPS 2024
Improving Deep Reinforcement Learning by Reducing the Chain Effect of Value and Policy Churn NIPS 2024
GTA: Generative Trajectory Augmentation with Guidance for Offline Reinforcement Learning NIPS 2024
Periodic agent-state based Q-learning for POMDPs NIPS 2024
Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data Corruptions NIPS 2024