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

… benchmark the performance of recent off-policy and batch reinforcement learning algorithms
… We find that under these conditions, many of these algorithms underperform DQN trained …

… have been the de facto benchmarking platforms. Besides RL, benchmarking platforms have
also … In this paper, we benchmark 11 MBRL algorithms and 4 MFRL algorithms across 18 …

… challenging testbed for reinforcement learning and continuous … of the strengths of existing
algorithms, but also reveal their … Furthermore, a range of reinforcement learning algorithms are …

… benchmark results, we analyse and discuss insights regarding the effectiveness of different
learning … The tasks we test in the benchmark paper are limited because of the algorithms’ …

… BBRL focuses on the core operations required to apply the comparison benchmark presented
in this paper. To do a complete experiment with the BBRL library, follow these five steps: …

… contributions to reinforcement learning benchmarking and … the challenges facing reinforcement
learning. The contributions … and provided algorithm implementations with benchmarks. …

… learning consisting of 50 distinct robotic manipulation tasks. Our aim is to make it possible to
develop algorithms … -art meta-reinforcement learning and multi-task learning algorithms on …

… state-of-the-art learning algorithms, and present some existing … -suite which we propose an
as an open-source benchmark. … these challenges, reinforcement learning can be more readily …

… direct applications of standard reinforcement learning (RL) algorithms like stable-baselines3. …
the scalability of RL algorithms for control. This project serves the learning for dynamics & …