Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning

Model-free algorithms such as TRPO, MAML-RL, PPO-related methods, and policy-gradient systems learn policies directly but are interaction-hungry. Model-based RL first fits dynamics and then plans, improving sample efficiency. Gaussian-process approaches address uncertainty but scale poorly and impose structural assumptions; high-capacity neural models scale better but usually adapt slowly. This paper makes rapid adaptation a primary training objective.

A robot may experience joint failure, terrain change, wind, payload variation, lighting, or pose-estimation error at any time. Training a separate policy for every possibility is infeasible. Online temporal-difference learning is too slow, and applying gradient-based dynamic evaluation to a normally trained model creates a training–deployment mismatch. The central problem is to infer a locally useful dynamics model from only M recent transitions.

• �� Temporal local tasks: every trajectory segment, rather than only a complete episode, may define a task. • Future-prediction meta-objective: past M transitions adapt the model and future K transitions score it. • GrBAL: MAML-style gradient adaptation of a neural forward model. • ReBAL: an RNN learns the adaptation rule. • Receding-horizon control: MPPI uses the adapted model and replans every step. • Real-robot validation: the approach is demonstrated on a dynamic legged millirobot.

• �� Data: sample τE(t−M,t−1) and τE(t,t+K) from experience collected across environment distribution ρ(E).
• �� Dynamics: model p̂θ(s′|s,a) as a Gaussian with neural-network mean; three hidden layers of 512 ReLU units and fixed variance make maximum likelihood equivalent to MSE.
• �� GrBAL: update θ using recent-transition log likelihood, then optimize the outer meta-loss on future transitions.
• �� ReBAL: use recurrent hidden state and gating to learn how observations modify the predictor.
• �� Control: pass θ′ to MPPI with reward r and horizon H; execute one action, append the transition, reset to θ*, and repeat.

The MuJoCo suite includes Half-cheetah with disabled joints, upward/downward slopes, and floating water blocks with varying damping and friction; Ant includes crippled legs and transitions from normal to crippled operation. Baselines are TRPO, MAML-RL, standard model-based RL (MB), and MB with dynamic evaluation (MB+DE). All model-based methods share architecture and bootstrapping; simulation uses MPPI and real-robot experiments use random shooting. GrBAL and ReBAL are also compared.

Both adaptive learners changed their local model and handled unseen failures, slopes, and contact dynamics better than nonadaptive MB and the reported alternatives. Meta-training used approximately 1.5–3 hours of equivalent real-world experience, around 10× less than model-free single-task learning. GrBAL was demonstrated on a real millirobot adapting to a missing leg, novel terrain, slopes, pose errors, calibration errors, and pulling payloads. The reported advantage is both sample efficiency and recovery speed.

The method is suited to legged robots, field inspection, warehouse mobility, agricultural machines, and assistive devices. Deployment requires shared observation and action spaces, representative meta-training environments, online transition data, and a compatible MPC planner such as MPPI or random shooting. Its immediate value is graceful recovery without retraining an entire policy after each physical change.

The local-consistency assumption can fail when dynamics switch faster than the adaptation window. Fixed variance does not fully represent predictive uncertainty, while neural updates and MPPI impose computation costs. A short-horizon adapted model may not support long-term planning, and severe failures outside the training distribution can remain unrecoverable. Future systems should add uncertainty calibration, change detection, safety constraints, cross-morphology transfer, and standardized return, latency, and compute benchmarks.