Reinforcement Learning
Better Exploration with Optimistic Actor Critic
Kamil Ciosek, Quan Vuong, Robert Loftin, Katja Hofmann
Neural Information Processing Systems http://nips.cc/
Regret Minimization for Reinforcement Learning with Vectorial Feedback and Complex Objectives
Due to state transitions, it is challenging to balance the contribution from each dimension for achieving near-optimality.
Finite-Sample Analysis for SARSA with Linear Function Approximation
Shaofeng Zou, Tengyu Xu, Yingbin Liang
SARSA is an on-policy algorithm to learn a Markov decision process policy in reinforcement learning. We investigate the SARSA algorithm with linear function approximation under the non-i.i.d.
Reconciling ฮป-Returns with Experience Replay
Brett Daley, Christopher Amato
A unique benefit to this approach is that each transition's TD error can be
Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning
Exploration in multi-agent reinforcement learning is a challenging problem, especially in environments with sparse rewards. We propose a general method for efficient exploration by sharing experience amongst agents.
Regret Bounds for Learning State Representations in Reinforcement Learning
Ronald Ortner, Matteo Pirotta, Alessandro Lazaric, Ronan Fruit, Odalric-Ambrym Maillard
Neural Information Processing Systems http://nips.cc/
