WebMar 1, 2024 · Model-Based Reinforcement Learning for Atari. Model-free reinforcement learning (RL) can be used to learn effective policies for complex tasks, such as Atari …

Fast and Data Efficient Reinforcement Learning from Pixels via …

WebThis need for sample efficiency is even more compelling when agents are deployed in the real world. A number of approaches have been proposed in the literature to address the sample inefficiency of deep RL algorithms. Broadly, they can be classified into two streams of research, though not mutually exclusive: (i) Auxiliary tasks on the agent ... WebJun 1, 2024 · “Our empirical evaluation of MiniGrid, MinAtar and Atari100K shows how Graph Backup boosts performance in the data-efficient setting. In particular, we improve … health partners clinic wabasha st paul

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WebFeb 1, 2024 · TL;DR: We investigate the feasibility of pretraining and cross-task transfer in model-based RL, and improve sample-efficiency substantially over baselines on the … WebFeb 1, 2024 · TL;DR: We investigate the feasibility of pretraining and cross-task transfer in model-based RL, and improve sample-efficiency substantially over baselines on the Atari100k benchmark. Abstract: Reinforcement Learning (RL) algorithms can solve challenging control problems directly from image observations, but they often require … WebWe present CURL: Contrastive Unsupervised Representations for Reinforcement Learning. CURL extracts high-level features from raw pixels using contrastive learning and performs off-policy control on top of the extracted features. CURL outperforms prior pixel-based methods, both model-based and model-free, on complex tasks in the DeepMind … healthpartners.com/patientregister