Goal-conditioned reinforcement learning hinges on how the goal is encoded. Contrastive, metric, temporal-distance, and information-theoretic encoders differ in objective. They still share one trait. None of them sees the current state. Such a state-independent embedding cannot mark which part of the goal still needs action. The policy must then recover that cue by inverting both encoders. We propose DAGR. It refines the static embedding of any late-fusion encoder into a state-conditioned one through multi-scale gated cross-attention. A near-identity gated residual preserves the base representation. Difference-aware Goal Cross-Attention then biases the attention scores using a per-token state-goal discrepancy map. On OGBench, DAGR improves navigation. Our ablations trace the gain to the gated residual, not to the difference bias that names the method. On manipulation and puzzle tasks it matches or falls below the base. DAGR is a structured refinement, not a universal improvement.
Does a reinforcement-learning agent that earns high reward actually learn its task's hidden state, or only a shortcut that correlates with reward? We build an instrument that makes this question directly measurable: the task is a hidden finite automaton that the agent partially controls. Because the automaton is known, the best achievable return is computable, and raw reward becomes a normalized score. The true state is also known at every step, so a linear probe can test whether the agent tracks a state it never observes. Measured separately, reward and state learning come apart: weak on-policy RL earns reward while the state probe stays at chance. Whether the agent escapes this shortcut depends on the optimizer, the training budget, and the task's structure. Structure gives an early warning: when the automaton is a permutation (group-language) automaton, a property readable from its transition table before any training, the agent will likely fail to learn the state. On a stratified set of 153 held-out automata the warning is right in 89 of the 103 cases where it fires; its absence, though, is no guarantee of safety. The instrument also tells apart two failures that reward alone cannot: a perception gap, where the linear probe finds no trace of a state the network could have represented, and a planning gap, where the state is decodable yet reward stays low. High reward alone is not evidence that the task's state was learned.
Jesper Klicks, Sander Vržina, Vincent François-Lavetcs.LG cs.AI
Energy trading decisions depend not only on current market prices, but also on expected future market conditions, and operational constraints. This makes the state representation given to a reinforcement learning agent an important design choice. We study this in HydroDam, a pumped-storage arbitrage environment, using a fixed Double DQN agent. The environment, action space, reward function, network, and training protocol are kept fixed; only the market features are changed. We compare absolute price/calendar features, relative features that compare current prices with recent market history, forecast features, and all combinations of these three feature families. Policies are trained and selected using 2007--2011 Belgian day-ahead prices and evaluated on two test settings: a later same-market test set from 2012--2025 and 39 other ENTSO-E market zones. Absolute features only reaches 28.8% on the test set and a median 5.7% across zones. Relative-only and forecast-only states also stay below a rolling price-score heuristic in the cross-zone median. Combining feature families is much stronger: absolute + relative reaches 49.9% on the test set and a 39.8% cross-zone median, while absolute + relative + forecast reaches 55.6% and 47.5%. These results suggest that state representation is not a minor preprocessing choice in storage-trading RL, but a central part of the policy design: robust transfer requires combining price scale, recent relative price context, and short-horizon forecast information, rather than relying on any single feature family.