Deep reinforcement learning (deep RL) agents have achieved remarkable success in a broad range of game-playing and continuous control tasks. While these agents are effective at maximizing rewards, it is often unclear what strategies they use to do so. In this paper, we take a step toward explaining deep RL agents through a case study in three Atari 2600 environments. In particular, we focus on understanding agents in terms of their visual attentional patterns during decision making. To this end, we introduce a method for generating rich saliency maps and use it to explain 1) what strong agents attend to 2) whether agents are making decisions for the right or wrong reasons, and 3) how agents evolve during the learning phase. We also test our method on non-expert human subjects and find that it improves their ability to reason about these agents. Our techniques are general and, though we focus on Atari, our long-term objective is to produce tools that explain any deep RL policy.
Recent work by @cite_16 has developed tools for explaining deep RL policies in visual domains. Similar to our work, the authors use the Atari 2600 environments as interpretable testbeds. Their key contribution is a method of approximating the behavior of deep RL policies via Semi-Aggregated Markov Decision Processes (SAMDPs). They use the more interpretable SAMDPs to gain insights about the higher-level temporal structure of the policy.
@cite_16
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In this paper, we propose to use deep policy networks which are trained with an advantage actor-critic method for statistically optimised dialogue systems. First, we show that, on summary state and action spaces, deep Reinforcement Learning (RL) outperforms Gaussian Processes methods. Summary state and action spaces lead to good performance but require pre-engineering effort, RL knowledge, and domain expertise. In order to remove the need to define such summary spaces, we show that deep RL can also be trained efficiently on the original state and action spaces. Dialogue systems based on partially observable Markov decision processes are known to require many dialogues to train, which makes them unappealing for practical deployment. We show that a deep RL method based on an actor-critic architecture can exploit a small amount of data very efficiently. Indeed, with only a few hundred dialogues collected with a handcrafted policy, the actor-critic deep learner is considerably bootstrapped from a combination of supervised and batch RL. In addition, convergence to an optimal policy is significantly sped up compared to other deep RL methods initialized on the data with batch RL. All experiments are performed on a restaurant domain derived from the Dialogue State Tracking Challenge 2 (DSTC2) dataset. Deep reinforcement learning (deep RL) agents have achieved remarkable success in a broad range of game-playing and continuous control tasks. While these agents are effective at maximizing rewards, it is often unclear what strategies they use to do so. In this paper, we take a step toward explaining deep RL agents through a case study in three Atari 2600 environments. In particular, we focus on understanding agents in terms of their visual attentional patterns during decision making. To this end, we introduce a method for generating rich saliency maps and use it to explain 1) what strong agents attend to 2) whether agents are making decisions for the right or wrong reasons, and 3) how agents evolve during the learning phase. We also test our method on non-expert human subjects and find that it improves their ability to reason about these agents. Our techniques are general and, though we focus on Atari, our long-term objective is to produce tools that explain any deep RL policy. Deep reinforcement learning (RL) algorithms have shown an impressive ability to learn complex control policies in high-dimensional environments. However, despite the ever-increasing performance on popular benchmarks such as the Arcade Learning Environment (ALE), policies learned by deep RL algorithms often struggle to generalize when evaluated in remarkably similar environments. In this paper, we assess the generalization capabilities of DQN, one of the most traditional deep RL algorithms in the field. We provide evidence suggesting that DQN overspecializes to the training environment. We comprehensively evaluate the impact of traditional regularization methods, @math -regularization and dropout, and of reusing the learned representations to improve the generalization capabilities of DQN. We perform this study using different game modes of Atari 2600 games, a recently introduced modification for the ALE which supports slight variations of the Atari 2600 games traditionally used for benchmarking. Despite regularization being largely underutilized in deep RL, we show that it can, in fact, help DQN learn more general features. These features can then be reused and fine-tuned on similar tasks, considerably improving the sample efficiency of DQN. We introduce an approach for deep reinforcement learning (RL) that improves upon the efficiency, generalization capacity, and interpretability of conventional approaches through structured perception and relational reasoning. It uses self-attention to iteratively reason about the relations between entities in a scene and to guide a model-free policy. Our results show that in a novel navigation and planning task called Box-World, our agent finds interpretable solutions that improve upon baselines in terms of sample complexity, ability to generalize to more complex scenes than experienced during training, and overall performance. In the StarCraft II Learning Environment, our agent achieves state-of-the-art performance on six mini-games -- surpassing human grandmaster performance on four. By considering architectural inductive biases, our work opens new directions for overcoming important, but stubborn, challenges in deep RL.