Research map / Reinforcement learning / Imitation learning
Environment Design for Inverse Reinforcement Learning
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's full text. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Proposes environment design for IRL that adaptively selects demonstration environments via maximin Bayesian regret, with ED-BIRL (tabular) and ED-AIRL (continuous) variants that improve sample-efficiency and robustness of learned rewards.
Problem
Learning reward functions from expert demonstrations is sample-inefficient, and IRL methods trained on a single fixed environment overfit to its dynamics and fail to generalise to slight dynamics changes.
Approach
The learner actively chooses which demonstration environment the expert acts in, each round picking the environment that maximises Bayesian regret (maximin) of the current best-guess reward, and extends BIRL and AIRL to demonstrations pooled across multiple environment dynamics.
Key results
- On the continuous control suite, ED-AIRL achieves the highest demo and test average normalised returns on continuous maze (68 demo / 71 test) versus DR-AIRL (52/53) and AIRL (33/52).
- In the discrete maze, ED-BIRL recovers all three goal squares after three rounds, while fixed-environment and domain-randomisation BIRL recover only one goal.
- ED-AIRL outperforms DR-AIRL on Swimmer (80 vs 45 demo) and matches it on HalfCheetah (both about 40 demo), while RIME fails on most tasks except Hopper.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| adaptively select demonstration environments using maximin regret instead of selecting environments uniformly at random (ablation) | AIRL-ME (ED-AIRL vs DR-AIRL) · Continuous maze | normalized return: Demo: 52±07 -> 68±04; Test: 53±12 -> 71±02 | Helped |
| adaptively select demonstration environments using maximin regret instead of selecting environments uniformly at random (ablation) | AIRL-ME (ED-AIRL vs DR-AIRL) · Hopper | normalized return: Demo: 59±06 -> 63±07; Test: 56±04 -> 52±04 | Mixed |
| adaptively select demonstration environments using maximin regret instead of selecting environments uniformly at random (ablation) | AIRL-ME (ED-AIRL vs DR-AIRL) · HalfCheetah | normalized return: Demo: 40±13 -> 40±11; Test: 40±11 -> 35±13 | No clear effect |
| adaptively select demonstration environments using maximin regret instead of selecting environments uniformly at random (ablation) | AIRL-ME (ED-AIRL vs DR-AIRL) · Swimmer | normalized return: Demo: 45±04 -> 80±19; Test: 53±05 -> 69±12 | Helped |
| learn from adaptively selected multiple environments rather than one fixed environment | AIRL-ME (ED-AIRL vs AIRL) · Continuous maze Test | normalized return: 52±07 -> 71±02 | Helped |
| learn from adaptively selected multiple environments rather than one fixed environment | AIRL-ME (ED-AIRL vs AIRL) · Hopper Test | normalized return: 34±04 -> 52±04 | Helped |
| learn from adaptively selected multiple environments rather than one fixed environment | AIRL-ME (ED-AIRL vs AIRL) · HalfCheetah Test | normalized return: 16±07 -> 35±13 | Helped |
| learn from adaptively selected multiple environments rather than one fixed environment | AIRL-ME (ED-AIRL vs AIRL) · Swimmer Test | normalized return: 44±08 -> 69±12 | Helped |
| adaptively design maze layouts for expert demonstrations rather than use a fixed maze or randomly generated mazes | ED-BIRL · Discrete maze | recovery of performance-relevant reward locations | Helped |
| use a fixed environment or domain randomization instead of adaptive environment design (ablation) | BIRL · Discrete maze | recovery of performance-relevant reward locations | Hurt |
| use adaptively designed demonstration environments rather than fixed-environment learning | BIRL · Randomly generated MDP test environments | utility loss | Helped |
| increase the number of expert trajectories while keeping AIRL in the base environment | AIRL · Continuous maze | normalized return | No clear effect |
Surprising findings
- Behavioral cloning and GAIL transfer surprisingly well on Swimmer, outperforming IRL methods there (BC 124 demo vs ED-AIRL 80).
- RIME performs very poorly on continuous maze and HalfCheetah, with negative returns, which the authors attribute to larger environment differences than in its original study.
Limitations
- ED-AIRL computational cost grows quadratically with number of rounds due to repeated policy optimisation.
- MaxEnt IRL inference (AIRL) was found to be unstable.
- ED-BIRL is restricted to simple tabular problems due to high complexity.
Experimental setup
Datasets: MuJoCo Hopper MuJoCo HalfCheetah MuJoCo Swimmer MuJoCo Ant continuous maze discrete maze (minigrid-inspired) randomly generated MDPs
Benchmarks: continuous maze MuJoCo Hopper MuJoCo HalfCheetah MuJoCo Swimmer discrete maze randomly generated MDPs
Metrics: normalised return on demo and test environments utility loss 25%-quantile return
Baselines: AIRL DR-AIRL RIME GAIL BC Fixed Environment BIRL Domain Randomisation BIRL
Builds on: Bayesian IRL Adversarial IRL Maximum Entropy IRL Robust Imitation learning with Multiple perturbed Environments (RIME) Proximal Policy Optimisation
Closest papers by meaning
- Recent Advancements in Inverse Reinforcement Learning (AAAI 2024 · similarity 0.90)
- Hybrid Inverse Reinforcement Learning (ICML 2024 · similarity 0.90)
- EvIL: Evolution Strategies for Generalisable Imitation Learning (ICML 2024 · similarity 0.89)
- Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective (ICML 2024 · similarity 0.89)
- Offline Inverse RL: New Solution Concepts and Provably Efficient Algorithms (ICML 2024 · similarity 0.88)
- How does Inverse RL Scale to Large State Spaces? A Provably Efficient Approach (NeurIPS 2024 · similarity 0.88)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.