Environment Agnostic Goal-Conditioning, A Study of Reward-Free Autonomous Learning

2025-11-06

AI Summary

This work addresses the challenge of autonomous goal setting and generalizable learning in reinforcement learning (RL) without external rewards. Methodologically, it reformulates standard RL environments into goal-conditioned ones and introduces an environment-agnostic, self-supervised goal-generation mechanism that requires no extrinsic reward and supports arbitrary observations as goals, while remaining compatible with off-policy RL algorithms. Its core contribution lies in decoupling goal generation from policy learning, enabling stable, reward-free exploration. Empirically, the approach achieves significantly higher average goal success rates compared to conventional methods—without increasing training time—and demonstrates strong cross-environment generalization across diverse, heterogeneous domains. These results validate both its environment independence and practical efficacy for unsupervised, goal-directed skill acquisition.

Technology Category

• Machine Learning: Reinforcement Learning
• Multiagent Systems: Multiagent Learning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In this paper we study how transforming regular reinforcement learning environments into goal-conditioned environments can let agents learn to solve tasks autonomously and reward-free. We show that an agent can learn to solve tasks by selecting its own goals in an environment-agnostic way, at training times comparable to externally guided reinforcement learning. Our method is independent of the underlying off-policy learning algorithm. Since our method is environment-agnostic, the agent does not value any goals higher than others, leading to instability in performance for individual goals. However, in our experiments, we show that the average goal success rate improves and stabilizes. An agent trained with this method can be instructed to seek any observations made in the environment, enabling generic training of agents prior to specific use cases.

Problem

• Developing reward-free autonomous learning through goal-conditioned environment transformation
• Enabling agents to self-select goals in environment-agnostic training scenarios
• Achieving stable average success rates across diverse goals without external guidance

Innovation

• Transforming environments into goal-conditioned setups
• Using environment-agnostic autonomous goal selection
• Employing reward-free off-policy learning algorithms