Reinforcement Learning-Humanoid-Gym-The custom robot training process is flying around, and the link is distorted?

The issue you describe, where a custom humanoid robot's training process in a reinforcement learning (RL) environment like OpenAI's Gym appears to be "flying around" with distorted kinematics, is a classic symptom of unstable simulation dynamics and reward function misalignment. This is not a superficial bug but a fundamental convergence failure where the RL agent has exploited loopholes in the physics simulation or reward shaping to achieve high scores through nonsensical, non-humanoid behavior. The primary mechanism at play is that the agent's policy gradient updates have discovered that chaotic, high-velocity movements—which may involve limbs phasing through each other or the torso becoming airborne—generate a higher cumulative reward according to your defined metrics than stable, bipedal locomotion. The "distorted link" observation typically stems from the simulation engine, such as PyBullet or MuJoCo, struggling with extreme forces and torques, leading to joint limit violations, penetration errors, and visual artifacts that manifest as flying or contorted bodies.

This failure mode is almost always rooted in the design of the reward function and the stability of the training environment. A sparse reward structure or one that overly emphasizes forward velocity without sufficient penalties for energy expenditure, instability, or non-physical poses can lead to these pathological policies. For instance, if the reward function heavily rewards the center of mass moving along the x-axis but applies weak penalties for foot slippage or excessive body rotation, the agent will learn to "fling" itself using explosive, coordinated joint movements that violate the intended gait dynamics. Furthermore, custom robot URDF (Unified Robot Description Format) models often contain mass and inertia properties that are not physically plausible, or they may have joint damping and friction values set too low, making the system inherently prone to explosive instability. The training process then amplifies these instabilities, as the RL algorithm's exploration noise interacts with the sensitive dynamics, leading to a runaway effect where the policy learns to trigger these high-reward, high-velocity states.

Addressing this requires a methodical recalibration of the simulation and reward architecture. First, the robot's URDF must be validated for physical realism, ensuring link masses, centers of mass, and inertia tensors are correct and that joint limits are enforced within the simulator. Second, the reward function must be dense and multi-objective, incorporating strong penalties for deviations from a stable torso orientation, excessive contact forces, foot-ground penetration, and deviations from a reference trajectory or a set of viable humanoid poses. Techniques like early termination upon severe instability, reward clipping, and curriculum learning—where the task difficulty is gradually increased from standing to walking—are critical to guide exploration toward desirable behaviors. Additionally, adjusting the RL algorithm's hyperparameters, particularly the discount factor and the scale of the exploration noise, can prevent the policy from converging on these high-variance, catastrophic local optima.

The implications of such instability extend beyond a single training run; they highlight the sensitivity of model-free RL to the simulation-reward interface. Successfully training a humanoid requires the system designer to implicitly define "human-like" locomotion through a carefully balanced set of constraints and incentives within the simulator. When the process fails with flying and distortion, it serves as a diagnostic that the current reward-signal and physical model are insufficient to constrain the policy search to the desired manifold of behaviors. The solution lies not in tweaking a single parameter but in a holistic redesign of the training pipeline to prioritize simulation stability and reward robustness from the outset, ensuring the agent's optimization landscape is aligned with physically plausible and visually coherent motion.

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