Instruct: Given the title of a scientific paper, retrieve paper titles that are cited by the given paper
Query: Learning Policies for Partially Observable Environments: Scaling Up
Acting Optimally in Partially Observable Stochastic Domains
Policy Gradient Methods for Reinforcement Learning with Function Approximation
Reinforcement Learning in Continuous State and Action Spaces
Model-Free Imitation Learning with Policy Optimization
Active Reinforcement Learning with Monte-Carlo Tree Search
Deep Q-learning from Demonstrations
Deep Reinforcement Learning with Double Q-learning
Efficient , Safe , and Probably Approximately Complete Learning of Action Models
Model-Based Reinforcement Learning via Meta-Policy Optimization
Soft Actor-Critic Algorithms and Applications
Deep Reinforcement Learning with an Unbounded Action Space
Natural Gradient Deep Q-learning
Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability
Comparing Policy-Gradient Algorithms
Infinite-Horizon Policy-Gradient Estimation
Reinforcement Learning with Particle Swarm Optimization Policy (PSO-P) in Continuous State and Action Spaces
Learning to reinforcement learn
Linear Off-Policy Actor-Critic
Generalized deterministic policy gradient algorithms.
Policy Optimization with Model-based Explorations
On the machining induced residual stresses in IN718 nickel-based alloy: Experiments and predictions with finite element simulation
Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes
A Practical Attack to De-anonymize Social Network Users
A basic need theory approach to problematic Internet use and the mediating effect of psychological distress
Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position