conftrace_
← Methods
Reinforcement Learning › Methods ›

Deep RL

3,861 papers

Papers per year

Papers

Policy Optimization in Adversarial MDPs: Improved Exploration via Dilated Bonuses NIPS 2021
Provably Efficient Actor-Critic for Risk-Sensitive and Robust Adversarial RL: A Linear-Quadratic Case AISTATS 2021
Multi-task Actor-Critic with Knowledge Transfer via a Shared Critic ACML 2021
Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning ICML 2021
Reinforcement Learning for Route Optimization with Robustness Guarantees IJCAI 2021
Towards Sample Efficient Agents through Algorithmic Alignment (Student Abstract) AAAI 2021
Learning to Reweight Imaginary Transitions for Model-Based Reinforcement Learning AAAI 2021
DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning AAAI 2021
Composable Energy Policies for Reactive Motion Generation and Reinforcement Learning RSS 2021
Exploiting Reasoning Chains for Multi-hop Science Question Answering EMNLP 2021
Theoretically Principled Deep RL Acceleration via Nearest Neighbor Function Approximation AAAI 2021
Guided Exploration with Proximal Policy Optimization using a Single Demonstration ICML 2021
GMAC: A Distributional Perspective on Actor-Critic Framework ICML 2021
Sequential Generative Exploration Model for Partially Observable Reinforcement Learning AAAI 2021
Decoupling Value and Policy for Generalization in Reinforcement Learning ICML 2021
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning ICML 2021
Transferable Dialogue Systems and User Simulators IJCNLP 2021
Reinforcement Learning with Prototypical Representations ICML 2021
Mastering Atari Games with Limited Data NIPS 2021
APS: Active Pretraining with Successor Features ICML 2021
Auxiliary Tasks for Efficient Learning of Point-Goal Navigation WACV 2021
Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices ICML 2021
Generalizable Episodic Memory for Deep Reinforcement Learning ICML 2021
EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL ICML 2021
RMM: Reinforced Memory Management for Class-Incremental Learning NIPS 2021