Research map / Reinforcement learning / Visual and representation RL
Position: Automatic Environment Shaping is the Next Frontier in RL
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A position paper arguing that manual environment shaping (reward, observation, action, initial state, termination design) is the main bottleneck for sim-to-real RL and that automating it should be the research priority, supported by IsaacGymEnvs ablations and a Eureka case study.
Problem
Sim-to-real RL for robotics requires heavy manual task-specific environment shaping, which bottlenecks scaling RL to diverse tasks and is not automated.
Approach
Frames environment shaping (reward, observation, action space, initial/goal states, termination) as a distinct, formalized bilevel optimization problem over a shaping function, and argues for automating it jointly rather than only reward design, with unshaped benchmarks.
Key results
- Removing shaping in IsaacGymEnvs drops Humanoid reward from 7554 to 0 with unshaped observation space, and AllegroHand from 38777 to 0 with sparse reward or no early termination.
- Eureka automating observation or action shaping alone matches human design (0.967 and 0.982 vs 0.973 on Anymal), but jointly shaping reward and observation drops to 0.196.
- Hill-climbing one shaping aspect at a time gets stuck in local optima differing by up to four shaping types.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use sparse reward instead of the shaped reward (ablation) | AllegroHand · AllegroHand | Reward: 38777 -> 0 | Hurt |
| Remove action-space shaping (ablation) | AllegroHand · AllegroHand | Reward: 38777 -> 21530 | Hurt |
| Remove observation-space shaping (ablation) | AllegroHand · AllegroHand | Reward: 38777 -> 2114 | Hurt |
| Remove early termination (ablation) | AllegroHand · AllegroHand | Reward: 38777 -> 0 | Hurt |
| Use a single initial state (ablation) | AllegroHand · AllegroHand | Reward: 38777 -> 0 | Hurt |
| Use a single goal state (ablation) | AllegroHand · AllegroHand | Reward: 38777 -> 141155 | Helped |
| Use sparse reward instead of the shaped reward (ablation) | Humanoid · Humanoid | Reward: 7554 -> 5237 | Hurt |
| Remove action-space shaping (ablation) | Humanoid · Humanoid | Reward: 7554 -> 67 | Hurt |
| Remove observation-space shaping (ablation) | Humanoid · Humanoid | Reward: 7554 -> 0 | Hurt |
| Remove early termination (ablation) | Humanoid · Humanoid | Reward: 7554 -> 705 | Hurt |
| Use a single initial state (ablation) | Humanoid · Humanoid | Reward: 7554 -> 5735 | Hurt |
| Use sparse reward instead of the shaped reward (ablation) | Anymal · Anymal | Reward: -45 -> -2789 | Hurt |
Surprising findings
- Eureka fails to improve when jointly optimizing reward and observation, despite succeeding on each individually.
- Single-goal-state variant for AllegroHand increases reward from 38777 to 141155, showing shaping choices can be counterproductive.
Limitations
- Position paper with limited empirical evidence restricted to three IsaacGymEnvs tasks and one LLM-based method.
- Unshaped environments are likely too hard for existing RL algorithms, which the paper acknowledges.
Experimental setup
Datasets: IsaacGymEnvs tasks (AllegroHand, Humanoid, Anymal)
Benchmarks: IsaacGymEnvs AllegroHand IsaacGymEnvs Humanoid IsaacGymEnvs Anymal
Metrics: episode return relative automation performance gain
Baselines: Eureka human-designed environment shaping
Builds on: Eureka PPO IsaacGymEnvs
Closest papers by meaning
- Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL (NeurIPS 2024 · similarity 0.87)
- Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes (AAAI 2025 · similarity 0.86)
- Scaling Offline Evaluation of Reinforcement Learning Agents through Abstraction (AAAI 2024 · similarity 0.84)
- Automaton Constrained Q-Learning (NeurIPS 2025 · similarity 0.84)
- Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks (ICLR 2024 · similarity 0.84)
- Staggered Environment Resets Improve Massively Parallel On-Policy Reinforcement Learning (NeurIPS 2025 · similarity 0.84)
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