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Augmenting Decision with Hypothesis in Reinforcement Learning
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Proposes ALH, which augments TD3 actor and critic with a learned hypothesis embedding (weak-model-based learning plus adaptive rollout) to fix slow exploitation and bias sensitivity in value-based RL, evaluated on MultiNormEnv and eight MuJoCo tasks.
Problem
Value-based RL (e.g., TD3) exploits slowly early in training because optimistic critic estimates favor exploration, and its policies are sensitive to critic bias from non-representative data.
Approach
Augment actor and critic with a hypothesis embedding produced by an independent weak model trained on observation sets, detached from actor/critic optimization, and update that hypothesis incrementally during rollout so decisions adapt to the latest interactions without changing network parameters.
Key results
- ALH-g reaches average normalized score 86.50 and ALH-a 85.46 on eight MuJoCo tasks, versus 67.27 for TD3, 36.99 for MBPO, 44.70 for DDPG and 23.40 for PPO.
- On HalfCheetah-v3, ALH-g achieves 8508.09 and ALH-a 7924.09 average return versus 7563.45 for TD3.
- Ablation on HalfCheetah shows removing adaptive rollout drops aggregate performance to about 88% of the full ALH-a method, the largest contribution among ablated components.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| augment TD3 with hypothesis representations and adaptive rollout | ALH-a · HalfCheetah-v3 | average return: 7563.45±996.14 -> 7924.09±636.61 | Helped |
| augment TD3 with hypothesis representations and adaptive rollout | ALH-g · Hopper-v3 | average return: 3273.77±285.26 -> 3439.50±145.63 | Helped |
| augment TD3 with hypothesis representations and adaptive rollout | ALH-g · Walker2d-v3 | average return: 3612.88±860.95 -> 4225.65±382.86 | Helped |
| augment TD3 with hypothesis representations and adaptive rollout | ALH-a · Ant-v3 | average return: 4884.83±675.88 -> 5395.55±353.28 | Helped |
| augment TD3 with hypothesis representations and adaptive rollout | ALH-a · Humanoid-v3 | average return: 5031.64±182.01 -> 5086.73±212.45 | Helped |
| augment TD3 with hypothesis representations and adaptive rollout | ALH-g · Reacher-v2 | average return: -6.06±0.63 -> -5.79±0.76 | Hurt |
| augment TD3 with hypothesis representations and adaptive rollout | ALH-a · InvertedDoublePendulum-v2 | average return: 7492.54±3732.47 -> 9358.61±2.57 | No clear effect |
| augment TD3 with hypothesis representations and adaptive rollout | ALH-a · InvertedPendulum-v2 | average return: 1000.00±0.00 -> 1000.00±0.00 | No clear effect |
| add detachment of hypothesis representation from Q and policy objective gradients (ablation) | ALH-g · HalfCheetah-v3 (Average aggregate) | normalized score: 91.71% -> 100% | Helped |
| add detachment of hypothesis representation from Q and policy objective gradients (ablation) | ALH-a · HalfCheetah-v3 (Average aggregate) | normalized score: 95.26% -> 100% | Helped |
| use localized hypotheses instead of one hypothesis from the current batch (ablation) | ALH-g · HalfCheetah-v3 (Average aggregate) | normalized score: 88.51% -> 100% | Helped |
| use localized hypotheses instead of one hypothesis from the current batch (ablation) | ALH-a · HalfCheetah-v3 (Average aggregate) | normalized score: 95.53% -> 100% | Helped |
Surprising findings
- Without detaching the hypothesis representation from actor/critic gradients, ALH-g performs roughly equal to TD3 on HalfCheetah.
Limitations
- Evaluated only with TD3 as the base learner; broader applicability to other RL algorithms left to future work.
- Bias experiments use a hand-constructed biased critic in a synthetic environment.
- Hypothesis-based robustness and validity definitions are informal and the weak-model criteria are not fully formalized.
Experimental setup
Datasets: MultiNormEnv MuJoCo gym tasks (HalfCheetah, Hopper, Walker2d, Ant, Humanoid, Reacher, InvertedDoublePendulum, InvertedPendulum)
Benchmarks: MultiNormEnv MuJoCo HalfCheetah-v3 Hopper-v3 Walker2d-v3 Ant-v3 Humanoid-v3 Reacher-v2 InvertedDoublePendulum-v2 InvertedPendulum-v2
Metrics: average return normalized score across methods standard deviation over 10 trials
Baselines: TD3 DDPG PPO MBPO
Builds on: TD3 MBPO DDPG PPO
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