Gürbüz ve uyarlanabilir derin pekiştirmeli öğrenme

This thesis addresses the challenge of developing robust and adaptive reinforcement learning (RL) agents that operate in complex, non-Markovian, and non-stationary environments. Unsupervised Meta-Testing with Conditional Neural Processes (UMCNP) addresses few-shot adaptation under unknown dynamics when reward signals are missing at test time. UMCNP learns a dynamics model to enable sample-efficient adaptation through self-generated trajectories. Episodic Return Progress with Bidirectional Progressive Neural Networks (ERP-BPNN) presents a human-inspired framework for multi-task learning by integrating a novel intrinsic motivation signal (ERP) for autonomous task switching with a bidirectional progressive neural network architecture, thereby facilitating effective skill transfer among morphologically different agents. State Reconstruction for Diffusion Policies (SRDP) confronts the challenge of generalization to out-of-distribution states in offline RL by incorporating a state reconstruction loss into the diffusion policy learning process. Forecasting in Non-stationary Offline RL (FORL) mitigates non-trivial non-stationarities by unifying conditional diffusion models with probabilistic zero-shot time-series foundation models. This framework proactively forecasts and corrects for abrupt, hidden observation offsets. Empirical evaluations across a range of continuous control, offline RL benchmarks, and robotics tasks confirm the efficacy of these methods. Our results demonstrate significant improvements in meta-testing sample efficiency, faster convergence via bidirectional skill transfer with return progress, superior generalization to out-of-distribution states, and robust performance against abrupt, non-Markovian shifts in the observation function.