We present a new technique for deep reinforcement learning that automatically detects moving objects and uses the relevant information for action selection. The detection of moving objects is done in an unsupervised way by exploiting structure from motion. Instead of directly learning a policy from raw images, the agent first learns to detect and segment moving objects by exploiting flow information in video sequences. The learned representation is then used to focus the policy of the agent on the moving objects. Over time, the agent identifies which objects are critical for decision making and gradually builds a policy based on relevant moving objects.

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In offline reinforcement learning (RL), the goal is to learn a highly rewarding policy based solely on a dataset of historical interactions with the environment. This serves as an extreme test for an agent's ability to effectively use historical data which is known to be critical for efficient RL. Prior work in offline RL has been confined almost exclusively to model-free RL approaches.

We present a new technique for deep reinforcement learning that automatically detects moving objects and uses the relevant information for action selection. The detection of moving objects is done in an unsupervised way by exploiting structure from motion. Instead of directly learning a policy from raw images, the agent first learns to detect and segment moving objects by exploiting flow information in video sequences. The learned representation is then used to focus the policy of the agent on the moving objects. Over time, the agent identifies which objects are critical for decision making and gradually builds a policy based on relevant moving objects.

Tremendous progress has been made in the development of reinforcement learning (RL) algorithms for tasks where the input consists of images.

Additional Feedback: Most of recent offline RL algorithms rely on policy regularization where the optimizing policy is prevented from deviating too much from the data-logging policy. Differently, MOReL does not directly rely on the data-logging policy but exploits pessimism to a model-based approach, providing another good direction for offline RL. However, it would be more natural to penalize more to more uncertain states. For example, one classical model-based RL algorithm (MBIE-EB) constructs an optimistic MDP that rewarding the uncertain regions by the bonus proportional to the 1/sqrt(N(s,a)) where N(s,a) is the visitation count. In contrast, but similarly to MBIE-EB, we may consider a pessimistic MDP that penalizes the uncertain regions by the penalty proportional to the 1/sqrt(N(s,a)). How is it justified to use alpha greater than zero for USAD? - It would be great to see how sensitive the performance of the algorithm with respect to kappa in the reward penalty and threshold in USAD.

All three reviewers have favourable opinion towards this paper. There are some minor questions or comments, but they can be addressed without requiring another round of reviewing. Therefore, I recommend acceptance of this work. I encourage the authors to incorporate the reviewers' comments and concerns as much as possible.

In offline reinforcement learning (RL), the goal is to learn a highly rewarding policy based solely on a dataset of historical interactions with the environment. This serves as an extreme test for an agent's ability to effectively use historical data which is known to be critical for efficient RL. Prior work in offline RL has been confined almost exclusively to model-free RL approaches. This framework consists of two steps: (a) learning a pessimistic MDP using the offline dataset; (b) learning a near-optimal policy in this pessimistic MDP. The design of the pessimistic MDP is such that for any policy, the performance in the real environment is approximately lower-bounded by the performance in the pessimistic MDP.