Reinforcement Learning in Sparse-Reward Environments With Hindsight Policy Gradients

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Goal-conditional policies are essential for reinforcement learning agents pursuing diverse objectives across episodes.
• Generalizing behavior to unseen goals and enabling hierarchical planning via subgoals are key benefits.
• Sparse-reward environments pose challenges for sample-efficient learning, necessitating effective goal achievement assessment.

Purpose of the Study:

• To introduce and generalize the concept of hindsight to policy gradient methods in reinforcement learning.
• To enhance the sample efficiency of reinforcement learning agents in sparse-reward settings.
• To enable agents to leverage information about goal achievement for improved learning.

Main Methods:

• Integrating hindsight into policy gradient algorithms.
• Generalizing the hindsight approach to a wide range of successful algorithms.
• Empirical evaluation across diverse sparse-reward environments.

Main Results:

• Demonstrated a significant increase in sample efficiency through the introduction of hindsight.
• Showcased the effectiveness of the generalized hindsight approach in policy gradient methods.
• Validated the approach across a variety of challenging sparse-reward tasks.

Conclusions:

• Hindsight is a crucial mechanism for improving sample efficiency in reinforcement learning, particularly in sparse-reward settings.
• The proposed method effectively integrates hindsight into policy gradient algorithms, offering broad applicability.
• This work advances the development of more capable and efficient reinforcement learning agents.