Using State Predictions for Value Regularization in Curiosity Driven Deep Reinforcement Learning
This work addresses the problem of slow learning in sparse reward environments for reinforcement learning agents, though it appears incremental as it builds on existing curiosity-driven methods.
The paper tackles the challenge of learning in sparse reward settings by extending a curiosity-driven agent to use state predictions directly for training, resulting in significantly faster learning in grid-world environments compared to baselines.
Learning in sparse reward settings remains a challenge in Reinforcement Learning, which is often addressed by using intrinsic rewards. One promising strategy is inspired by human curiosity, requiring the agent to learn to predict the future. In this paper a curiosity-driven agent is extended to use these predictions directly for training. To achieve this, the agent predicts the value function of the next state at any point in time. Subsequently, the consistency of this prediction with the current value function is measured, which is then used as a regularization term in the loss function of the algorithm. Experiments were made on grid-world environments as well as on a 3D navigation task, both with sparse rewards. In the first case the extended agent is able to learn significantly faster than the baselines.