offline-reinforcement-learning-methods

Fisher-BRC

Fisher-BRC is an algorithm used for offline reinforcement learning. It is based on actor-critic methods that encourage the learned policy to stay close to the data. The algorithm uses a neural network to learn the state-action value offset term, which can help regularize the policy changes. Actor-critic algorithm The actor-critic algorithm is a combination of two models - an actor and a critic. The actor is responsible for taking actions in the environment, and the critic is responsible for e

Recurrent Replay Distributed DQN

R2D2: A Revolutionary Approach to Reinforcement Learning Reinforcement Learning (RL) is a type of machine learning where an algorithm learns to make decisions by interacting with its environment. In recent years, RL has made significant strides in various fields such as robotics, gaming, and healthcare. One such advancement is the development of R2D2, a novel approach to training RL agents. What is R2D2? R2D2 stands for Recurrent Replay Distributed DQN, a state-of-the-art RL approach. It was

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