Hindsight Reward Tweaking via Conditional Deep Reinforcement Learning
This addresses the challenge of reward function design in RL for complex tasks, though it appears incremental as it builds on existing deep RL methods.
The paper tackles the problem of expensive reward function evaluation in reinforcement learning by proposing a hindsight reward tweaking approach that models reward influences within a near-optimal space, demonstrating feasibility with MuJoCo tasks.
Designing optimal reward functions has been desired but extremely difficult in reinforcement learning (RL). When it comes to modern complex tasks, sophisticated reward functions are widely used to simplify policy learning yet even a tiny adjustment on them is expensive to evaluate due to the drastically increasing cost of training. To this end, we propose a hindsight reward tweaking approach by designing a novel paradigm for deep reinforcement learning to model the influences of reward functions within a near-optimal space. We simply extend the input observation with a condition vector linearly correlated with the effective environment reward parameters and train the model in a conventional manner except for randomizing reward configurations, obtaining a hyper-policy whose characteristics are sensitively regulated over the condition space. We demonstrate the feasibility of this approach and study one of its potential application in policy performance boosting with multiple MuJoCo tasks.