This paper reviews exploration techniques in deep reinforcement learning. Exploration
techniques are of primary importance when solving sparse reward problems. In sparse …

Thompson sampling is an algorithm for online decision problems where actions are taken
sequentially in a manner that must balance between exploiting what is known to maximize …

Abstract Modern Reinforcement Learning (RL) is commonly applied to practical problems
with an enormous number of states, where\emph {function approximation} must be deployed …

This paper studies model-based reinforcement learning (RL) for regret minimization. We
focus on finite-horizon episodic RL where the transition model $ P $ belongs to a known …

Abstract Model-free reinforcement learning (RL) algorithms directly parameterize and
update value functions or policies, bypassing the modeling of the environment. They are …

We introduce NoisyNet, a deep reinforcement learning agent with parametric noise added to
its weights, and show that the induced stochasticity of the agent's policy can be used to aid …

Off-policy deep reinforcement learning (RL) has been successful in a range of challenging
domains. However, standard off-policy RL algorithms can suffer from several issues, such as …

While policy-based reinforcement learning (RL) achieves tremendous successes in practice,
it is significantly less understood in theory, especially compared with value-based RL. In …

Deep reinforcement learning (RL) methods generally engage in exploratory behavior
through noise injection in the action space. An alternative is to add noise directly to the …

Dealing with uncertainty is essential for efficient reinforcement learning. There is a growing
literature on uncertainty estimation for deep learning from fixed datasets, but many of the …