Multi-step learning applies lookahead over multiple time steps and has p...
Plasticity, the ability of a neural network to quickly change its predic...
Hierarchical Reinforcement Learning (HRL) agents have the potential to
d...
We study the learning dynamics of self-predictive learning for reinforce...
We study the multi-step off-policy learning approach to distributional R...
We present BYOL-Explore, a conceptually simple yet general approach for
...
In multi-agent reinforcement learning, the problem of learning to act is...
Exploration is essential for solving complex Reinforcement Learning (RL)...
We introduce Bootstrap Your Own Latent (BYOL), a new approach to
self-su...
Learning a good representation is an essential component for deep
reinfo...
As humans we are driven by a strong desire for seeking novelty in our wo...
In this paper we refine the process of computing calibration functions f...
In this paper we study a model-based approach to calculating approximate...
Motivated by value function estimation in reinforcement learning, we stu...