Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks
in which the agent has only limited environmental feedback. Despite many advances over …

Reinforcement learning (RL) provides a theoretical framework for continuously improving an
agent's behavior via trial and error. However, efficiently learning policies from scratch can be …

We learn end-to-end point-to-point and pathfollowing navigation behaviors that avoid
moving obstacles. These policies receive noisy lidar observations and output robot linear …

Automatic Curriculum Learning (ACL) has become a cornerstone of recent successes in
Deep Reinforcement Learning (DRL). These methods shape the learning trajectories of …

Recent literature has proposed approaches that learn control policies with high performance
while maintaining safety guarantees. Synthesizing Hamilton-Jacobi (HJ) reachable sets has …

Reinforcement Learning and recently Deep Reinforcement Learning are popular methods
for solving sequential decision making problems modeled as Markov Decision Processes …

We introduce an automatic curriculum algorithm, Variational Automatic Curriculum Learning
(VACL), for solving challenging goal-conditioned cooperative multi-agent reinforcement …

Reinforcement learning (RL) problems can be challenging without well-shaped rewards.
Prior work on provably efficient RL methods generally proposes to address this issue with …

Deep learning has achieved remarkable successes in solving challenging reinforcement
learning (RL) problems when dense reward function is provided. However, in sparse reward …

Many continuous control tasks have easily formulated objectives, yet using them directly as
a reward in reinforcement learning (RL) leads to suboptimal policies. Therefore, many …