End-to-End Reinforcement Learning Framework

Updated 14 October 2025

End-to-end RL frameworks are unified models that directly map raw sensory inputs to actions, eliminating the need for hand-engineered intermediate modules.
They jointly learn representations and policies through techniques like recurrent networks, cross-modal token fusion, and latent action space discovery for robust control.
These systems incorporate hybrid learning, curriculum strategies, and neurosymbolic integrations to overcome sample inefficiencies and enhance interpretability.

End-to-end reinforcement learning (RL) frameworks are defined by the direct optimization of a policy that maps raw environmental observations (e.g., raw sensor signals or language utterances) to high-level or low-level actions using global, often sparse, evaluative signals. Unlike modular pipelines, where stages such as feature extraction, state estimation, policy planning, and control are designed and optimized separately, end-to-end RL systems parameterize the entire perception-to-action chain as a single trainable model. This unified approach enables joint learning of representations and policies, facilitates the emergence of complex functions without hand-engineered intermediates, and supports adaptation to changing or diverse task specifications. Recent advances demonstrate that end-to-end RL frameworks can span a wide spectrum of domains, from dialog management and robotic manipulation to compiler optimization and agentic decision-making.

1. Architectural Principles and Unification

A core principle of end-to-end RL frameworks is the elimination of hand-engineered intermediate representations or fixed modular boundaries. Architectures are constructed as monolithic or weakly modular neural networks that directly ingest raw input modalities and output actions or action distributions. Prominent instantiations include:

Deep Recurrent Q-Network-based Architectures: As exemplified by the DRQN-based dialog system, perception (e.g., via an LSTM accumulating dialog turns), state tracking, and action selection policies are all subsumed within a single differentiable model (Zhao et al., 2016).
Morphology-agnostic Encoders and Decoders: In domains with variable embodiment, such as multi-leg robotics, unified architectures use attention-based pooling and joint description vectors to support variable input/output cardinality, as in URMA (Unified Robot Morphology Architecture) (Bohlinger et al., 10 Sep 2024).
Cross-modal Token Fusion: In vision-driven motion control, recent frameworks (e.g., those built on SSD-Mamba2) tokenize and fuse proprioceptive and exteroceptive data using state-space backbones, circumventing the scalability bottlenecks of transformers for long temporal or spatial horizons (Tao et al., 9 Sep 2025).
Neurosymbolic and Hierarchical Pipelines: Object-centric world models, relational extractors, and symbolic rule regressors can be composed into interpretable, concept-bottlenecked end-to-end RL agents, preserving both performance and transparency (Grandien et al., 18 Oct 2024).

End-to-end RL frameworks also frequently incorporate asymmetric actor-critic architectures, curriculum learning, and experience replay modules to facilitate stable optimization, fast convergence, and rich exploration.

2. Joint Representation and Policy Learning

The hallmark advantage of end-to-end RL is the ability to jointly optimize internal representations and policies for the global reward signal, as opposed to tuning submodules in isolation. This jointness is realized at multiple levels:

Emergent Functions: Networks trained end-to-end with realistic sensorimotor streams autonomously develop functionalities such as memory, selective attention, coordinate transformations, active perception, and internal simulation (Shibata, 2017).
Implicit State Estimation: Recurrent or attention-based structures learn to maintain belief states or internal state vectors that encode sufficient statistics for sequential decision-making under partial observability (Zhao et al., 2016, Grandien et al., 18 Oct 2024).
Latent Action Space Discovery: In dialog and language-based systems, unsupervised latent variable modeling (e.g., using variational autoencoders or Gumbel-Softmax methods) allows the system to “invent” structured action spaces optimized for policy learning, bypassing limitations of word-level or handcrafted dialog act spaces (Zhao et al., 2019).
Transferable Embeddings: In locomotion and manipulation, encoders learn task- and morphology-agnostic embeddings (e.g., robust keypoints in manipulation (Wang et al., 2022), code embeddings in compiler optimization (Haj-Ali et al., 2019)) that support generalization and zero/few-shot adaptation.

This ability to learn distributed, task-relevant, and robust intermediate representations is crucial for scalability, versatility, and sim-to-real transfer.

3. Sample Efficiency, Hybridization, and Reward Design

Pure end-to-end reinforcement learning is sample-inefficient in sparse, long-horizon, or partially supervised settings. Contemporary frameworks address this through several mechanisms:

Hybrid RL–Supervised Learning: Augmenting the RL signal with supervised objectives when ground-truth annotations are available (e.g., for slot-filling or state tracking) accelerates convergence, partly by mitigating reward sparsity (Zhao et al., 2016).
Intrinsic and Structured Rewards: Model-based intrinsic rewards—such as learned distance-to-goal functions in CostNet (Andersen et al., 2022)—shape the exploration landscape, enhancing sample efficiency compared to standard model-free RL. Hierarchical or task-specific reward designs, sometimes aided by LLMs (e.g., LLM-guided reward code in AnyBipe (Yao et al., 13 Sep 2024)), are increasingly used for rapid iteration and alignment with user-defined objectives.
Experience Replay and Failure-driven Replay: Frameworks like DeepTravel (Ning et al., 26 Sep 2025) maintain a buffer of failed experiences and periodically replay them, ensuring the agent learns from hard cases, which improves generalization and autonomy in complex compositional task spaces.

Table: Representative End-to-End RL Techniques and Sample Efficiency Enhancements

Technique/Module	Sample Efficiency Solution	Domain
Hybrid RL-SL loss	Direct supervised updates on labels	Dialog
Intrinsic distance reward	Learn cost-to-go; guide exploration	Grid-world
Curriculum learning	Progressive task difficulty	Robotics
Experience replay/failure buf	Replay hard or rare cases in RL updates	Agentic TL
LLM-guided reward synthesis	Generate/refine reward code autonomously	Robotics

4. Generalization, Transfer, and Robustness

End-to-end RL frameworks exhibit a range of mechanisms to support generalizability across tasks, embodiments, and environmental conditions:

Domain Randomization: During training, properties such as robot masses, friction coefficients, sensor noise, and actuator latencies are randomized to produce policies robust to sim-to-real gaps and hardware variation (Wang et al., 18 Jun 2025, Wang et al., 2022).
Morphology-agnostic Design: Architectures such as URMA separate “joint description” from physical configuration, allowing learned policies to generalize to unseen robot morphologies with zero/few-shot adaptation and transfer (Bohlinger et al., 10 Sep 2024).
Curriculum and Hierarchical Reward Schemes: Progressive curricula, as in flying through narrowing gaps (Xiao et al., 2021), or hierarchical spatial-temporal verifiers, as in travel planning (Ning et al., 26 Sep 2025), aid in decomposing long-horizon or compositional tasks, thus improving policy robustness.
Multi-task and Agentic Capabilities: Frameworks such as DeepTravel (Ning et al., 26 Sep 2025) and Graph-R1 (Luo et al., 29 Jul 2025) support autonomous, context-sensitive, multi-step tool usage and interaction, with RL-based reward funnels that enforce both trajectory-level and turn-level feasibility.

Empirical evaluation consistently demonstrates strong gains in downstream metrics (e.g., win rate in dialog, pass rate in travel planning, mean episode return/traveled distance in locomotion) and significant improvements in sim-to-real transfer reliability.

5. Practical Applications and Deployment

End-to-end RL frameworks have reached a maturity level suitable for deployment in diverse real-world tasks:

Robotics: Open-source tools such as Booster Gym enable direct transfer from simulation to omnidirectional walking, disturbance resistance, and terrain adaptation on real humanoid robots (Wang et al., 18 Jun 2025). Key design elements include an asymmetric actor-critic, rich domain randomization, and robust policy export via JIT compilation.
Autonomous Driving: Semantic representation learning combined with distributed RL enables real-time deployment on autonomous vehicles, achieving high safety and intervention metrics under diverse environmental conditions (Wang et al., 2021).
Compiler Optimization: NeuroVectorizer demonstrates automatic selection of optimal vectorization and interleaving factors in compilers, leading to substantial performance speedups over hand-engineered heuristics (Haj-Ali et al., 2019).
Dialog and Language Agents: End-to-end RL systems, including large-scale GNN-based policies for negotiation (Renting et al., 21 Jun 2024), support transfer across diverse problem sizes and domains by leveraging graph-based action and observation spaces.

The infrastructural elements—such as GPU-accelerated simulators, modular SDKs abstracting hardware, and large-scale task generators—facilitate repeatable, scalable, and community-accessible training and deployment pipelines.

6. Interpretability, Neurosymbolic Extensions, and Limitations

While end-to-end RL offers high flexibility and performance, it introduces challenges in transparency and control:

Neurosymbolic Integration: The SCoBots framework (Grandien et al., 18 Oct 2024) establishes an interpretable end-to-end RL agent by explicitly splitting processing into object extraction, relational reasoning, and rule-based policy distillation (via tools like ECLAIRE), allowing extraction and inspection of symbolic IF-THEN rules from neural agents.
Boundary of Functionality: In fully end-to-end setups, internal “functions” (e.g., recognition, attention, control) emerge in a distributed fashion not easily delimited or mapped to traditional submodules (Shibata, 2017). This confounds direct interpretability, debugging, and safety assurance.
Sample and Compute Cost: End-to-end RL remains computationally intensive and may require richly instrumented environments, especially in high-dimensional or multi-modal domains (vision, language, control).
Reward Dependence and Engineering: The quality and granularity of learned behaviors hinge on the availability and fidelity of reward signals. Modern frameworks address this through LLM-driven reward engineering (Yao et al., 13 Sep 2024), intrinsic modeling (Andersen et al., 2022), and hierarchical verifiers (Ning et al., 26 Sep 2025), but challenges in reward misspecification persist.

This suggests ongoing research is directed toward integrating symbolic reasoning, improving data and compute efficiency, and automating the design of reward and curriculum structures to enhance both performance and interpretability.

7. Outlook and Future Directions

Recent developments indicate that end-to-end RL frameworks are converging toward:

Foundation Models for Control: Morphology-agnostic policy architectures supporting zero/few-shot transfer across a broad suite of robots (Bohlinger et al., 10 Sep 2024).
Integrated Agentic Reasoning: Agentic frameworks—e.g., DeepTravel and Graph-R1—formulate reasoning, tool use, and planning as multi-round agent-environment interactions, optimized via end-to-end RL, and demonstrate advances in practical tool-use and factuality (Luo et al., 29 Jul 2025, Ning et al., 26 Sep 2025).
Automated Reward/Policy Engineering: LLM-guided reward function synthesis dramatically lowers the barrier to task alignment and policy iteration for complex robotic systems (Yao et al., 13 Sep 2024).
Interpretable Policy Extraction: Neurosymbolic RL and interpretable policy distillation (e.g., SCoBots) provide a basis for policy verification and safety-critical assurance (Grandien et al., 18 Oct 2024).

A plausible implication is that as these frameworks scale in architectural generality and training data diversity, end-to-end RL is becoming a principal paradigm for embodied intelligence, enabling “learned” general-purpose control and decision policies that can be robustly, transparently, and efficiently deployed in real-world, safety-critical, and open-ended environments.