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An Optimistic Perspective on Offline Reinforcement Learning An Optimistic Perspective on Offline Reinforcement Learning International Conference on Machine Learning (ICML) 2020. An earlier version was titled "Striving for Simplicity in Off-Policy Deep Reinforcement Learning" and presented as a contributed talk at NeurIPS 2019 Deep RL Workshop. RL Unplugged (tfds) tfds (Colab) DQN Replay Dataset Blog Paper Code Talk Slides Pytorch (Example 1) Pytorch (Example 2) Authors DQN Replay Dataset The DQN Replay Dataset was collected as follows: We first train a DQN agent, on all 60 Atari 2600 games with sticky actions enabled for 200 million frames (standard protocol) and save all of the experience tuples of (observation, action, reward, next observation) (approximately 50 million) encountered during training. We repeat this process five times for each game. Important notes on Atari ROM versions The DQN replay dataset is generated using a legacy set ...
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