Why Standard Navigation Models Leave Value on the Table
Autonomous robot navigation has made considerable strides through DRL, in which an agent learns to navigate by interacting with its environment and receiving rewards or penalties for its actions. Unlike map-dependent approaches such as simultaneous localization and mapping (SLAM), which rely on prebuilt environmental maps and can fail in unknown environments, DRL-based mapless navigation enables a robot to find its way using only raw sensor data and a defined goal.
However, a persistent inefficiency has held back many DRL-based systems, which is uniform experience sampling. Most approaches treat every past interaction as equally useful, diluting the rare but highly instructive moments, narrowly avoiding a collision, discovering a novel path, or successfully reaching a target. This underrepresentation forces agents to accumulate enormous numbers of training steps before meaningfully improving.
Rethinking How a Robot Learns from Experience
The central innovation of GER-RL lies in replacing uniform experience sampling with a goal-guided, greedy prioritization mechanism based on the temporal-difference (TD) error. The TD error for any given experience measures the gap between the value the agent's current network assigns to a state-action pair and the value estimated by a target network.
A large TD error signals that the experience contains something surprising, something the agent's current understanding cannot yet account for, making it especially valuable for learning. GER-RL ranks all stored experiences by their TD error and samples them with a probability inversely proportional to their rank, ensuring that high-value experiences are revisited far more frequently than routine ones.
To prevent this non-uniform sampling from distorting the training process, the researchers introduced importance-sampling weights to correct for the altered distribution of selected experiences. These weights grow progressively over the course of training, providing stronger corrections as the agent matures. The prioritization balance itself is governed by a tunable parameter set to 0.6 in experiments.
The method is built on the twin delayed deep deterministic policy gradient (TD3) algorithm, an architecture well-suited to continuous action spaces and specifically designed to overcome overestimation and policy instability problems found in earlier approaches.
The model's actor network receives light detection and ranging (LIDAR) sensor readings and the polar coordinates of the navigation target, outputting linear and angular velocity commands. The accompanying critic network evaluates the quality of those commands, and the two networks refine each other iteratively through the actor-critic training loop.
Equally important is a carefully engineered reward function that shapes the agent's behavior beyond simple goal-seeking. Reaching the target earns a large positive reward, while a collision triggers a large penalty. Every timestep carries a small negative penalty to discourage idling; a positive reward proportional to the agent's forward speed encourages active movement; and an additional penalty is applied when the robot strays too close to an obstacle, promoting proactive avoidance rather than reactive swerving.
Putting GER-RL to the Test
The researchers evaluated GER-RL in a Gazebo simulation environment using a robot operating across 1,000 test episodes, with four randomly positioned obstacles regenerated each episode to prevent the model from memorizing fixed layouts. G-TD3, the base navigation model, goal-driven autonomous exploration (GDAE), an established benchmark from prior literature, and GER-RL, the proposed method, were compared. Performance was assessed across accuracy, average step length, average reward, collision rate, and average episode duration.
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GER-RL achieved an 80% success rate, outperforming both G-TD3 (75%) and GDAE (74%). Its collision rate of 17% was the lowest across all three models, compared to 18% for G-TD3 and 20% for GDAE. In terms of efficiency, GER-RL completed episodes in an average of 93.66 steps and 12.35s, against G-TD3's 108.67 steps and 14.17s, and GDAE's 100.79 steps and 14.65s. The average reward of 76.84 also exceeded G-TD3's 65.17, confirming that the agent was not only reaching its target more often but doing so more smoothly and efficiently.
Training dynamics further illustrated GER-RL's learning quality. In early training episodes, reward values were predominantly negative, reflecting frequent collisions and timeouts. As training progressed, the proportion of positive rewards steadily increased, eventually stabilizing between 100 and 150. The pattern reflects an agent that, thanks to prioritized replay, encounters and learns from its most consequential experiences far earlier and more reliably than a uniformly sampling counterpart would.
The Road Ahead
GER-RL represents a meaningful advance in data-efficient reinforcement learning for autonomous navigation. By ensuring that an agent's most informative experiences are revisited proportionally to their learning value, the method accelerates policy improvement without requiring additional environment interactions or computational resources.
The pairing of prioritized experience replay with a compound reward function and a stable TD3 backbone produces a navigation system that is at once safer, faster, and more reliable than its predecessors. The authors acknowledge that current experiments are confined to flat, two-dimensional simulation environments and note that real-world conditions remain unaddressed. Future work aims to test the approach on physical robots and extend it to handle the full complexity of dynamic, uncertain real-world environments.
Journal Reference
Zeng, Y., & Xie, M. (2026). Goal-guided greedy experience replay-enhanced reinforcement learning for efficient autonomous navigation. Scientific Reports. DOI:10.1038/s41598-026-51502-1, https://www.nature.com/articles/s41598-026-51502-1
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