rail-berkeley/softlearning
Softlearning is a reinforcement learning framework for training maximum entropy policies in continuous domains. Includes the official implementation of the Soft Actor-Critic algorithm.
This is a specialized deep reinforcement learning toolbox for AI/ML researchers and engineers to develop and test 'maximum entropy policies' for autonomous agents in simulated continuous environments. You input environmental data and agent behaviors, and it outputs optimized control policies for tasks like robotic movement or control systems. This is for those who are developing and evaluating advanced AI agents in complex, simulated scenarios.
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Use this if you are a researcher or engineer working on advanced reinforcement learning algorithms for continuous control tasks in simulated environments.
Not ideal if you are a practitioner looking for an off-the-shelf solution to a specific real-world automation problem, or if you prefer PyTorch over TensorFlow.
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Nov 29, 2023
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