The idea is to augment Monte-Carlo TreeSearch (MCTS) withmaximum entropypolicyoptimization, evaluatingeach search node bysoftmax values back-propagated from simulation.

A scene may have long range non-local interactions, rotation equivariance may be violated by a preferred camera angle, or a dynamical system may occasionally have discontinuous transitions.

Even if the optimal policy is obtained precisely, it is often the case the optimal policy is deterministic.

Most of these advancements came through the continual development of new algorithms, which were designed using a combination of mathematical derivations, intuitions, and experimentation. Such an approach of creating algorithms manually is limited by human understanding and ingenuity.

Reinforcement learning framework and algorithms implemented in PyTorch. To get started, checkout the example scripts, linked above. The initial release for 0.2 has the following major changes: Overall, the refactors are intended to make the code more modular and readable than the previous versions. These Anaconda environments use MuJoCo 1.5 and gym 0.10.5. You'll need to get your own MuJoCo key if you want to use MuJoCo.

We are announcing the release of our state-of-the-art off-policy model-free reinforcement learning algorithm, soft actor-critic (SAC). This algorithm has been developed jointly at UC Berkeley and Google Brain, and we have been using it internally for our robotics experiment. Soft actor-critic is, to our knowledge, one of the most efficient model-free algorithms available today, making it especially well-suited for real-world robotic learning. We also release our implementation of SAC, which is particularly designed for real-world robotic systems. What makes an ideal deep RL algorithm for real-world systems?