深度强化学习长回合训练管理及基于状态的探索策略咨询

Your idea to tie exploration rate to state novelty instead of time-based decay is extremely reasonable—in fact, it addresses two major flaws of standard ε-greedy in your specific scenario:

1. Time-decaying ε fails when training restarts: If your agent crashes early and restarts, it resets the time counter, but the agent already has prior experience. You end up wasting cycles re-exploring states it already knows, while missing exploration when it encounters new, critical states later.
2. It can't adapt to state shifts: When the environment throws a sudden, unforeseen state (like step 1000 in your case), a time-decayed ε is already too low to prioritize exploring this new regime, leading to poor performance and more unnecessary restarts.

By linking ε to how "new" a state is, you align exploration with the actual uncertainty of the environment. This is the core of effective exploration in non-stationary or long-horizon tasks—explore more when you're in uncharted territory, exploit when you know what works.

Here are concrete ways to turn this idea into code:

• Quantify state novelty:
  • Similarity-based checks: Use your replay buffer to compute the distance between the current state and stored states (cosine similarity for high-dimensional time series, Euclidean distance for low-dimensional). The larger the minimum distance, the more novel the state.
  • Count-based approximation: For continuous state spaces, use kernel density estimation or state clustering to track how often each "state group" is visited. Lower counts mean higher novelty. For discrete states, just track raw visit counts.
  • Encoder-based novelty: Train a lightweight autoencoder to map states to a low-dimensional latent space, then compute novelty using distances in this latent space. This is way more efficient for high-dimensional time series.
• Dynamic ε adjustment:
  • Set hard bounds for ε (e.g., ε_min = 0.01, ε_max = 0.6) to avoid extreme values.
  • Use a smooth mapping from novelty to ε—for example:

    # sigmoid-based mapping to adjust sensitivity
    ε = ε_min + (ε_max - ε_min) * sigmoid(k * (novelty_score - threshold))
    

    Tune k and threshold based on how quickly you want ε to respond to new states.
  • After a restart: Don't reset ε to the initial high value. Instead, start with a moderate ε (e.g., 0.3) and let it adjust based on the states the agent encounters—this leverages prior training experience.
• Combine with intrinsic motivation:
  • Add a small intrinsic reward for visiting novel states. This complements the ε adjustment by actively driving the agent to explore new regions, which is critical for 100k-step long horizons.

Here are key papers that validate and expand on your approach:

• Count-Based Exploration with Neural Density Models: Replaces raw state counting with neural networks to estimate state visit density, making count-based exploration feasible for continuous state spaces. Directly ties exploration rate to state rarity.
• Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play: Uses state novelty as an intrinsic reward signal to drive exploration in non-stationary environments—perfect for your scenario with sudden state shifts.
• Deep Exploration via Bootstrapped DQN: Uses disagreement between multiple Q-networks to measure state uncertainty (a form of novelty). You can use this disagreement score to adjust your ε, instead of just raw state similarity.
• Adaptive ε-Greedy Exploration in Reinforcement Learning: An early foundational work that formalizes dynamic ε adjustment based on state visit frequency, proving that state-based exploration outperforms time-based decay in non-stationary tasks.

内容的提问来源于stack exchange，提问作者T.L