Sample efficiency has been one of the major challenges for deep reinforcement learning. Non-parametric episodic control has been proposed to speed up parametric reinforcement learning by rapidly latching on previously successful policies. However, previous work on episodic reinforcement learning neglects the relationship between states and only stored the experiences as unrelated items. To improve sample efficiency of reinforcement learning, we propose a novel framework, called Episodic Reinforcement Learning with Associative Memory (ER-LAM), which associates related experience trajectories to enable reasoning effective strategies. We build a graph on top of states in memory based on state transitions and develop an efficient reverse-trajectory propagation strategy to allow rapid value propagation through the graph. We use the non-parametric associative memory as early guidance for a parametric reinforcement learning model. Results on Atari games show that our framework has significantly higher sample efficiency and outperforms state-of-the-art episodic reinforcement learning models.
The paper proposes a new method for organizing episodic memory in with deep Q-networks. It organizes the memory as a graph in which nodes are internal representations of observed states and edges link state transitions. Additionally, nodes from different episodes are merged into a single node if they represent the same state, allowing for inter-episode value propagation of stored rewards. The authors experimentally evaluate the method against reasonable baselines and show improved performance in the majority of tasks.
While the proposed method seemingly leads to better performance, the analysis of the results appears superficial. First, it lacks theoretical rigor to explain why the proposed propagation mechanism works, for instance, a proof of the optimal substructure in Eq. (3) would be helpful. While I see the authors mentioned previous work which do not have optimal substructures and mention this for the case of navigation-like tasks, the matter is not discussed and unclear in other scenarios. 
This in itself would not be much of an issue if the experiments highlighted advantages and limitations of the proposed method, but that is not the case. For instance, per-task comparisons to EVA (mentioned in the paper) could indicate how useful inter-episode value propagation was in each task.  Table 1 mentions EVA, but only on aggregated results, thus not providing insight into this. Furthermore, the experiments selected to be detailed in Figure 4 are, in my opinion, suboptimal choices, as the worst-performing of the selected tasks is still better than the baselines. It would be more insightful to also compare those whose performance is close to the baselines, such as the Boxing environment, and significantly worse than baselines, such as the StarGunner environment (as shown in Figure 3). With those issues in mind, my conclusion is to recommend to reject the paper in the current state.
Minor comments:
- Thoroughly review the writing and grammar, the text in its current state needs significant improvement in this regard
- Equation references missing parentheses
- Introduction, 1st paragraph, second sentence is incomplete
- Algorithm 2: tuples taken from/appended to sets G and D are not consistent (cf. lines 11 and 13)
- Figure 3: The large amount of bars in the plot would benefit from horizontal lines across the plot for each tick on the y axis
- Table 1: caption before the table; should also state the meaning of numbers (percentage or normalized score, calculated from what?)
- Figure 4: labels (all text except plot titles) are impossible to read in print
- Section 3 could be placed before Section 2, laying the mathematical framework, and then following the discussion with related work
- There is redundant content in Sections 2 and 3
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I am happy with the authors response and changed the score accordingly