ProgAgent:A Continual RL Agent with Progress-Aware Rewards

• URL: http://arxiv.org/abs/2603.07784v1
• Date: Sun, 08 Mar 2026 19:58:07 GMT
• Title: ProgAgent:A Continual RL Agent with Progress-Aware Rewards
• Abstract summary: ProgAgent is a continual reinforcement learning agent that unifies progress-aware reward learning with a JAX-native system architecture.<n>It derives dense, shaped rewards from unlabeled expert videos through a perceptual model that estimates task progress across initial, current, and goal observations.<n>ProgAgent supports massively parallel rollouts and fully differentiable updates, rendering a sophisticated unified objective feasible.
• Score: 0.07646713951724009
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present ProgAgent, a continual reinforcement learning (CRL) agent that unifies progress-aware reward learning with a high-throughput, JAX-native system architecture. Lifelong robotic learning grapples with catastrophic forgetting and the high cost of reward specification. ProgAgent tackles these by deriving dense, shaped rewards from unlabeled expert videos through a perceptual model that estimates task progress across initial, current, and goal observations. We theoretically interpret this as a learned state-potential function, delivering robust guidance in line with expert behaviors. To maintain stability amid online exploration - where novel, out-of-distribution states arise - we incorporate an adversarial push-back refinement that regularizes the reward model, curbing overconfident predictions on non-expert trajectories and countering distribution shift. By embedding this reward mechanism into a JIT-compiled loop, ProgAgent supports massively parallel rollouts and fully differentiable updates, rendering a sophisticated unified objective feasible: it merges PPO with coreset replay and synaptic intelligence for an enhanced stability-plasticity balance. Evaluations on ContinualBench and Meta-World benchmarks highlight ProgAgent's advantages: it markedly reduces forgetting, boosts learning speed, and outperforms key baselines in visual reward learning (e.g., Rank2Reward, TCN) and continual learning (e.g., Coreset, SI) - surpassing even an idealized perfect memory agent. Real-robot trials further validate its ability to acquire complex manipulation skills from noisy, few-shot human demonstrations.

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