In this paper we combine one method for hierarchical reinforcement learning - the options framework - with deep Q-networks (DQNs) through the use of different "option heads" on the policy network, and a supervisory network for choosing between the different options. We utilise our setup to investigate the effects of architectural constraints in subtasks with positive and negative transfer, across a range of network capacities. We empirically show that our augmented DQN has lower sample complexity when simultaneously learning subtasks with negative transfer, without degrading performance when learning subtasks with positive transfer.
Another notable success in subtask learning with multiple independent sources of reward are universal value function approximators (UVFAs) @cite_0 . UVFAs allow the generalisation of value functions across different goals, which helps the agent accomplish tasks that it has never seen before. The focus of UVFAs is in generalising between similar subtasks by sharing the representation between the different tasks. This has recently been expanded upon in the hierarchical-DQN @cite_4 ; however, these goal-based approaches have been demonstrated in domains where the different goals are highly related. From a function approximation perspective, goals should share a lot of structure with the raw states. In contrast, our approach focuses on separating out distinct subtasks, where partial independence between subpolicies can be enforced through structural constraints. In particular, we expect that separate Q-functions are less prone to negative transfer between subtasks.
@cite_0 @cite_4
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Value functions are a core component of reinforcement learning systems. The main idea is to to construct a single function approximator V (s; θ) that estimates the long-term reward from any state s, using parameters θ. In this paper we introduce universal value function approximators (UVFAs) V (s, g; θ) that generalise not just over states s but also over goals g. We develop an efficient technique for supervised learning of UVFAs, by factoring observed values into separate embedding vectors for state and goal, and then learning a mapping from s and g to these factored embedding vectors. We show how this technique may be incorporated into a reinforcement learning algorithm that updates the UVFA solely from observed rewards. Finally, we demonstrate that a UVFA can successfully generalise to previously unseen goals. We describe a framework for multitask deep reinforcement learning guided by policy sketches. Sketches annotate each task with a sequence of named subtasks, providing high-level structural relationships among tasks, but not providing the detailed guidance required by previous work on learning policy abstractions for RL (e.g. intermediate rewards, subtask completion signals, or intrinsic motivations). Our approach associates every subtask with its own modular subpolicy, and jointly optimizes over full task-specific policies by tying parameters across shared subpolicies. This optimization is accomplished via a simple decoupled actor–critic training objective that facilitates learning common behaviors from dissimilar reward functions. We evaluate the effectiveness of our approach on a maze navigation game and a 2-D Minecraft-inspired crafting game. Both games feature extremely sparse rewards that can be obtained only after completing a number of high-level subgoals (e.g. escaping from a sequence of locked rooms or collecting and combining various ingredients in the proper order). Experiments illustrate two main advantages of our approach. First, we outperform standard baselines that learn task-specific or shared monolithic policies. Second, our method naturally induces a library of primitive behaviors that can be recombined to rapidly acquire policies for new tasks. We describe a framework for multitask deep reinforcement learning guided by policy sketches. Sketches annotate each task with a sequence of named subtasks, providing high-level structural relationships among tasks, but not providing the detailed guidance required by previous work on learning policy abstractions for RL (e.g. intermediate rewards, subtask completion signals, or intrinsic motivations). Our approach associates every subtask with its own modular subpolicy, and jointly optimizes over full task-specific policies by tying parameters across shared subpolicies. This optimization is accomplished via a simple decoupled actor-critic training objective that facilitates learning common behaviors from dissimilar reward functions. We evaluate the effectiveness of our approach on a maze navigation game and a 2-D Minecraft-inspired crafting game. Both games feature extremely sparse rewards that can be obtained only after completing a number of high-level subgoals (e.g. escaping from a sequence of locked rooms or collecting and combining various ingredients in the proper order). Experiments illustrate two main advantages of our approach. First, we outperform standard baselines that learn task-specific or shared monolithic policies. Second, our method naturally induces a library of primitive behaviors that can be recombined to rapidly acquire policies for new tasks. We consider the problem of learning from sparse and underspecified rewards, where an agent receives a complex input, such as a natural language instruction, and needs to generate a complex response, such as an action sequence, while only receiving binary success-failure feedback. Such success-failure rewards are often underspecified: they do not distinguish between purposeful and accidental success. Generalization from underspecified rewards hinges on discounting spurious trajectories that attain accidental success, while learning from sparse feedback requires effective exploration. We address exploration by using a mode covering direction of KL divergence to collect a diverse set of successful trajectories, followed by a mode seeking KL divergence to train a robust policy. We propose Meta Reward Learning (MeRL) to construct an auxiliary reward function that provides more refined feedback for learning. The parameters of the auxiliary reward function are optimized with respect to the validation performance of a trained policy. The MeRL approach outperforms our alternative reward learning technique based on Bayesian Optimization, and achieves the state-of-the-art on weakly-supervised semantic parsing. It improves previous work by 1.2 and 2.4 on WikiTableQuestions and WikiSQL datasets respectively.