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Latent Reward: LLM-Empowered Credit Assignment in Episodic Reinforcement Learning
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Proposes LaRe, which uses GPT-4o-generated executable code as multi-dimensional "latent rewards" with self-verification, and uses them inside return decomposition to improve temporal and multi-agent credit assignment in episodic RL.
Problem
Episodic RL gives only delayed, sparse episodic returns, making credit assignment (reward redistribution) hard; prior return-decomposition methods suffer from redundant reward-irrelevant state features and ignore the multifaceted nature of performance evaluation.
Approach
Use an LLM (GPT-4o) to write code encoding environment states into a small set of interpretable latent reward dimensions, verified for executability and refined by self-prompting, and then fit a reward decoder on these latent features via least-squares return decomposition to produce proxy rewards for RL.
Key results
- On MPE Cooperative-Navigation and Predator-Prey tasks, LaRe (built on RD) outperforms all return-decomposition and multi-agent baselines (RD, IRCR, Diaster, RRD, AREL, STAS) across 6, 15, and 30 agents.
- Pre-verification raised the code execution rate of LLM-generated latent reward functions from 0-40% without it to 100% across the four evaluated tasks.
- Latent rewards correlate more strongly with ground-truth dense rewards than raw states (e.g., CN 0.50 with 5.6 dims vs. 0.02 with 26 states), and LaRe matches or exceeds TD3/IPPO trained with dense rewards on some tasks.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace raw states with LLM-derived latent rewards | Reward correlation with ground-truth dense rewards · CN (6 agents) | Pearson correlation: 0.02 -> 0.50 | Helped |
| replace raw states with LLM-derived latent rewards | Reward correlation with ground-truth dense rewards · PP (6 agents) | Pearson correlation: 0.01 -> 0.12 | Helped |
| replace raw states with LLM-derived latent rewards | Reward correlation with ground-truth dense rewards · HalfCheetah-v4 | Pearson correlation: 0.22 -> 0.53 | Helped |
| replace raw states with LLM-derived latent rewards | Reward correlation with ground-truth dense rewards · HumanoidStandup-v4 | Pearson correlation: 0.20 -> 0.49 | Helped |
| add pre-verification to generated latent-reward code (ablation) | LaRe · CN (6 agents) | Execution rate: 0% -> 100% | Helped |
| add pre-verification to generated latent-reward code (ablation) | LaRe · PP (6 agents) | Execution rate: 20% -> 100% | Helped |
| add pre-verification to generated latent-reward code (ablation) | LaRe · HalfCheetah-v4 | Execution rate: 40% -> 100% | Helped |
| add pre-verification to generated latent-reward code (ablation) | LaRe · HumanoidStandup-v4 | Execution rate: 40% -> 100% | Helped |
| remove agent-level decomposition and average agents' proxy rewards at each timestep (ablation) | LaRe · MPE tasks | Average episode return | Hurt |
| remove the reward decoder and use signed latent rewards as proxy rewards (ablation) | LaRe · Episodic RL tasks | Average episode return | Hurt |
| remove self-prompting from LLM generation (ablation) | LaRe · Episodic RL tasks | Average episode return | Hurt |
| replace LLM-derived latent rewards with a variational information bottleneck encoder (ablation) | LaRe · Episodic RL tasks | Average episode return | Hurt |
Surprising findings
- LaRe, trained only on episodic rewards, sometimes matches or exceeds policies trained with ground-truth dense rewards.
- Without pre-verification, LLM-generated latent reward functions frequently failed to execute (0% execution for CN with 6 agents).
Limitations
- Restricted to tasks with symbolic (non-image) states; extension to image-based tasks left to multi-modal LLMs.
- Relies on the LLM's task knowledge and task documentation; prompt design still requires task-specific state descriptions.
- Random variables set deterministically for convenience; theoretical bounds rely on assumptions such as existence of a latent function with exact reward representation.
Experimental setup
Datasets: MuJoCo (Reacher-v4, Walker2d-v4, HalfCheetah-v4, HumanoidStandup-v4) Multi-Agent Particle Environment (Cooperative-Navigation, Predator-Prey) SMAC Triangle Area (newly designed)
Benchmarks: MuJoCo MPE SMAC Triangle Area
Metrics: average episode return Pearson correlation coefficient code execution rate
Baselines: TD3 RD IRCR RRD RRD_unbiased Diaster AREL STAS IPPO VIB
Builds on: RUDDER randomized return decomposition Variational Information Bottleneck TD3 IPPO
Closest papers by meaning
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