… and multitask learning consisting of 50 distinct robotic manipulation tasks. Our aim is to … We
evaluate 6 state-of-the-art meta-reinforcement learning and multi-task learning algorithms on …

… In this work, we present a benchmark suite of continuous control tasks, including classic …
evaluation of a range of implemented reinforcement learning algorithms. Both the benchmark …

… This paper will propose more comprehensive and reasonable evaluation metrics for the
Atari benchmark to test the real superhuman reinforcement learning algorithms. Learning …

… In this paper, we propose a benchmark called RL Unplugged to evaluate and compare
offline RL methods. RL Unplugged includes data from a diverse range of domains including …

… , we introduce six reinforcement learning tasks based on three … benchmark four
reinforcement learning algorithms for continuous control: TRPO, PPO, DDPG, and Soft Q-learning …

… algorithms have been shown to fail in the batch setting–learning from a fixed data set … In
this paper, we benchmark the performance of recent off-policy and batch reinforcement learning …

… While current benchmark reinforcement learning (RL) tasks … in many ways poor substitutes
for learning with real-world data. By … Most benchmarks and datasets used to evaluate machine …

… We then evaluate recent state-of-the-art offline RL algorithms on NeoRL. The empirical … in
the previous benchmarks. We also disclose that current offline policy evaluation methods could …

… Reinforcement Learning setting where an agent’s interaction with the environment is modeled
through a Markov Decision Process (MDP) [63]. In this work, we benchmark … to evaluate a …

… We evaluate algorithms in two matrix games and four multi-agent environments, in … benchmark
results, we analyse and discuss insights regarding the effectiveness of different learning …