Learning Long-Term Reward Redistribution via Randomized Return Decomposition
This addresses the challenge of sparse rewards in reinforcement learning for applications requiring long-term planning, though it appears to be an incremental improvement within existing frameworks.
The paper tackles the problem of learning from sparse and delayed rewards in episodic reinforcement learning by proposing a novel reward redistribution algorithm called randomized return decomposition (RRD) that learns a proxy reward function, and demonstrates substantial improvement over baseline algorithms on benchmark tasks.
Many practical applications of reinforcement learning require agents to learn from sparse and delayed rewards. It challenges the ability of agents to attribute their actions to future outcomes. In this paper, we consider the problem formulation of episodic reinforcement learning with trajectory feedback. It refers to an extreme delay of reward signals, in which the agent can only obtain one reward signal at the end of each trajectory. A popular paradigm for this problem setting is learning with a designed auxiliary dense reward function, namely proxy reward, instead of sparse environmental signals. Based on this framework, this paper proposes a novel reward redistribution algorithm, randomized return decomposition (RRD), to learn a proxy reward function for episodic reinforcement learning. We establish a surrogate problem by Monte-Carlo sampling that scales up least-squares-based reward redistribution to long-horizon problems. We analyze our surrogate loss function by connection with existing methods in the literature, which illustrates the algorithmic properties of our approach. In experiments, we extensively evaluate our proposed method on a variety of benchmark tasks with episodic rewards and demonstrate substantial improvement over baseline algorithms.