Research map / Reinforcement learning / Visual and representation RL
Unveiling the Significance of Toddler-Inspired Reward Transition in Goal-Oriented Reinforcement Learning
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Proposes a toddler-inspired sparse-to-potential-based-dense (S2D) reward transition curriculum for goal-oriented RL and shows it improves sample efficiency, success, generalization, and leads to smoother policy loss landscapes and wider minima.
Problem
How reward density transitions during training (sparse-to-dense, inspired by toddler learning) affect RL sample efficiency, success rate, generalization, and loss landscape geometry in goal-oriented tasks.
Approach
Curriculum that switches from sparse rewards to potential-based dense rewards (distance potential) at a fixed early time point, preserving optimal policies, plus a Cross-Density Visualizer for post-transition 3D policy loss landscapes and a sharpness metric to link transitions to wide minima.
Key results
- On UR5-Reacher, S2D (C3) reaches 94.15 performance vs 64.23 for Only Dense and 0.00 for Only Sparse, with lowest sharpness 0.61.
- On LunarLander, S2D (C3) achieves 168.88 performance with sharpness 7.46, versus 142.50 for Only Sparse and 139.68 for Only Dense.
- Minima depth on UR5 drops from 0.095 to 0.033 in phase P5 after S2D transition, indicating a smoother loss landscape.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| transition from sparse to potential-based dense rewards | SAC · LunarLander | performance: 142.50 -> 168.88 | Helped |
| transition from sparse to potential-based dense rewards | SAC · CartPole-Reacher | performance: 3.88 -> 14.61 | Helped |
| transition from sparse to potential-based dense rewards | SAC · UR5-Reacher | performance: 64.23 -> 94.15 | Helped |
| transition from sparse to potential-based dense rewards | SAC · LunarLander | sharpness: 8.97 -> 7.46 | Helped |
| transition from sparse to potential-based dense rewards | SAC · CartPole-Reacher | sharpness: 0.19 -> 0.01 | Helped |
| transition from sparse to potential-based dense rewards | SAC · UR5-Reacher | sharpness: 0.67 -> 0.61 | Helped |
| vary the S2D transition timing across C1, C2, and C3 (ablation) | SAC · LunarLander | performance: 138.71\stdv{3.71}; 63.40\stdv{160.55}; 168.88\stdv{23.66} | Mixed |
| vary the S2D transition timing across C1, C2, and C3 (ablation) | SAC · CartPole-Reacher | performance: 3.18\stdv{4.0}; 14.61\stdv{10.96}; 5.29\stdv{7.472} | Mixed |
| vary the S2D transition timing across C1, C2, and C3 (ablation) | SAC · UR5-Reacher | performance: 65.54\stdv{10.86}; 65.69\stdv{17.32}; 94.15\stdv{4.28} | Mixed |
Surprising findings
- Sparse-only rewards yield zero performance on UR5-Reacher, while the sparse-to-dense transition recovers strong performance.
- Intrinsic-motivation baselines (NGU, RND) prioritized state coverage over goal attainment, underperforming goal-directed S2D.
Limitations
- No automatic method for finding the optimal reward transition timing; timing is tuned by ablation.
- Sharpness and loss-landscape links to generalization are correlational rather than causal.
- Experiments limited to a few environments with relatively small-scale tasks and a handful of seeds.
Experimental setup
Datasets: LunarLander-V2 CartPole-Reacher UR5-Reacher ViZDoom Seen/Unseen RWARE Gridworld
Benchmarks: LunarLander-V2 CartPole-Reacher UR5-Reacher ViZDoom-Seen ViZDoom-Unseen
Metrics: episode return/performance sample efficiency success rate sharpness (SAM-style worst-case loss increase) local minima depth
Baselines: Only Sparse Only Dense D2S NGU RND
Builds on: potential-based reward shaping Soft Actor-Critic A3C Random Network Distillation Never Give Up
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