TLDR: The research paper introduces Discrepancy of Environment Prioritized Experience Replay (DEER), a novel method designed to improve reinforcement learning (RL) in non-stationary environments where dynamics and rewards change over time. DEER addresses the limitations of traditional experience replay by proposing a metric called Discrepancy of Environment (DoE), which isolates the impact of environmental shifts on value functions. By using a binary classifier to detect environmental changes and applying distinct prioritization strategies for experiences collected before and after these shifts, DEER enables more sample-efficient learning. Experiments show that DEER significantly outperforms existing state-of-the-art experience replay methods, particularly in highly non-stationary settings, by improving performance and accelerating adaptation.
Reinforcement Learning (RL) has shown remarkable success in various applications, enabling agents to learn optimal behaviors through trial and error. However, a significant challenge arises when these agents operate in real-world environments that are constantly changing, known as non-stationary environments. In such dynamic settings, the environment’s rules, or ‘dynamics,’ and the rewards it offers can shift over time, quickly rendering past experiences obsolete and hindering efficient learning.
Traditional RL methods often rely on ‘Experience Replay’ (ER), a technique that stores and reuses past interactions (transitions) to improve data efficiency and stabilize learning. A common approach within ER is ‘TD-error prioritization,’ where experiences that lead to larger prediction errors are replayed more frequently, as they are considered more informative. While effective in stable environments, this method struggles in non-stationary ones because it cannot differentiate between changes caused by the agent’s own learning (policy updates) and those stemming from the environment itself. This can lead to the agent prioritizing outdated or irrelevant experiences, slowing down adaptation.
To tackle this critical issue, researchers have introduced a novel framework called Discrepancy of Environment Prioritized Experience Replay (DEER). This innovative approach aims to make RL agents more robust and sample-efficient in unpredictable conditions. At its core, DEER introduces a new metric: the Discrepancy of Environment (DoE).
Understanding Discrepancy of Environment (DoE)
The DoE metric is designed to specifically quantify the impact of environmental changes on the agent’s understanding of state-action values. Unlike TD-error, DoE isolates the effects of environment shifts by measuring the difference in the expected future rewards (Q-function) for a given action in a given state, both before and after an environmental change, while carefully excluding the effects of policy improvements. This allows DEER to precisely attribute value changes to the underlying environmental dynamics.
How DEER Works
DEER operates by first detecting when the environment’s dynamics have shifted. It achieves this by employing a binary classifier that analyzes reward sequences from adjacent time windows. If the classifier identifies a significant change in these sequences, it signals an environmental shift. Once a change is detected, DEER adapts its prioritization strategy:
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For Pre-Change Transitions: Experiences collected before the environmental shift are prioritized if they exhibit a *low* DoE. This is because low DoE indicates that these older experiences are less affected by the environmental change and thus remain more relevant to the current learning task.
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For Post-Change Transitions: Experiences collected after the environmental shift are prioritized using a hybrid strategy. This strategy combines the traditional TD-error (for policy refinement) with real-time DoE-based density differences. When the environment is still highly dynamic (indicated by a high density ratio score), transitions with elevated DoE are prioritized to help the agent adapt quickly. As the agent adapts and the environment stabilizes (lower density ratio score), the prioritization shifts back towards higher TD-error to refine the policy.
This adaptive mechanism ensures that DEER maintains a diverse replay buffer and dynamically allocates sampling priorities to meet the agent’s evolving needs, balancing the reuse of relevant old experiences with the rapid incorporation of new, crucial information.
Experimental Validation
The effectiveness of DEER was rigorously tested using the Soft Actor-Critic (SAC) algorithm on four standard MuJoCo continuous control tasks (Ant, HalfCheetah, Hopper, and Inverted Double Pendulum). To simulate non-stationary environments, researchers introduced varying offsets to friction and joint damping coefficients. DEER was compared against several state-of-the-art experience replay methods, including PER, RB-PER, CER, and LA3P.
The results were compelling: DEER consistently achieved higher overall returns and demonstrated superior adaptability. It exhibited less reduction in rewards and significantly faster recovery rates after environmental changes compared to other methods. Notably, in highly non-stationary settings (e.g., a 200% offset in environmental parameters), DEER achieved an impressive 22.53% higher rewards than the best-performing baseline. Even under mild non-stationarity, DEER maintained a performance edge, and in stationary environments, it performed comparably to other methods, indicating no negative impact when changes are absent.
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Conclusion
The Discrepancy of Environment Prioritized Experience Replay (DEER) framework represents a significant advancement in making reinforcement learning more practical and efficient in the face of real-world unpredictability. By intelligently prioritizing experiences based on both policy updates and environmental shifts, DEER enables RL agents to adapt more quickly and effectively to dynamic environments. This research opens new avenues for developing more robust AI systems capable of operating in complex and ever-changing conditions. For more details, you can read the full research paper here.
