Co-occurring keywords · papers mentioning both
Papers
QPLEX: Duplex Dueling Multi-Agent Q-Learning
ICLR 2021
Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning
ICML 2021
Reinforcement Learning with a Disentangled Universal Value Function for Item Recommendation
AAAI 2021
Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification
NeurIPS 2021
SEAGuL: Sample Efficient Adversarially Guided Learning of Value Functions
L4DC 2021
Self-Consistent Models and Values
NeurIPS 2021
Spectral Normalisation for Deep Reinforcement Learning: An Optimisation Perspective
ICML 2021
Symbolic Dynamic Programming for Continuous State MDPs with Linear Program Transitions
IJCAI 2021
Synthesis of Search Heuristics for Temporal Planning via Reinforcement Learning
AAAI 2021
Taylor Expansion of Discount Factors
ICML 2021
Theoretically Principled Deep RL Acceleration via Nearest Neighbor Function Approximation
AAAI 2021
The Value-Improvement Path: Towards Better Representations for Reinforcement Learning
AAAI 2021
Towards Hyperparameter-free Policy Selection for Offline Reinforcement Learning
NeurIPS 2021
TripleTree: A Versatile Interpretable Representation of Black Box Agents and their Environments
AAAI 2021
Twice regularized MDPs and the equivalence between robustness and regularization
NeurIPS 2021
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning
ICML 2021
A Boolean Task Algebra for Reinforcement Learning
NeurIPS 2020
A Closer Look at Deep Policy Gradients
ICLR 2020
Adaptive Trade-Offs in Off-Policy Learning
AISTATS 2020
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms
AISTATS 2020
Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions
AISTATS 2020
BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning
NeurIPS 2020
Deep Neural Network Approximated Dynamic Programming for Combinatorial Optimization
AAAI 2020
Deterministic Value-Policy Gradients
AAAI 2020
DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction
NeurIPS 2020