Critic-Free Policies with Vanilla PPO Loss

• The paper demonstrates that simplified PPO loss using clipped surrogate objectives enables stable policy updates even with minimal advantage feedback.
• Reformulations using hinge loss and perceptron-like objectives allow robust off-policy training while eliminating reliance on explicit value critics.
• Regularization techniques, early stopping, and partial evaluation improve sample efficiency and convergence, making critic-free PPO a competitive RL method.

Critic-Free Policies Using Vanilla PPO Loss refer to reinforcement learning policies optimized via the standard clipped surrogate objective of Proximal Policy Optimization (PPO), yet without reliance on explicit value network critics for advantage estimation, or, more generally, with minimal or non-parametric advantage feedback. These approaches are motivated by the theoretical and empirical observation that critical components of PPO—clipping-based regularization, multiple epochs of sample reuse, and structural surrogate losses—can confer sufficient stability for effective policy learning even in the absence of a sophisticated critic. Emerging lines of research demonstrate that vanilla PPO loss can be reformulated, extended, or tuned to produce critic-free or critic-minimal algorithms with desirable performance, sample efficiency, and theoretical guarantees.

1. Vanilla PPO Clipped Surrogate Objective

The core of PPO is the clipped surrogate objective: $$L_\text{CLIP}(\theta) = \mathbb{E}_t \left[ \min \left( r_t(\theta) A_t,\, \text{clip}\left(r_t(\theta), 1-\epsilon, 1+\epsilon\right) A_t \right) \right]$$ where $$r_t(\theta) = \pi_\theta(a_t|s_t)/\pi_{\text{old}}(a_t|s_t)$$ is the policy likelihood ratio, and $A_t$ is the advantage, typically estimated via generalized advantage estimation (GAE) or simple trajectory returns.

The clipped ratio framework automatically regularizes the update magnitude, dispenses with the need for trust-region constraints (as in TRPO), and allows several optimization epochs per batch of samples, thus leveraging sample reuse for improved efficiency (Schulman et al., 2017). The empirical stability of vanilla PPO loss is sufficient to maintain monotonic improvement trajectories and robust convergence on standard benchmarks, including MuJoCo locomotion and Atari control tasks, even when advantage estimates are produced using non-critic recipes (e.g., direct returns).

2. Reformulation and Hinge Loss Perspectives

Recent analysis formally links the PPO-Clip surrogate to large-margin classification objectives. The surrogate can be cast as a hinge loss: $$\ell\big(\text{sgn}[A],\, r-1,\, \epsilon\big) = \max\{0,\, \epsilon - \text{sgn}[A](r-1)\}$$ which enables policy improvement by only requiring the sign of advantage rather than its precise value (Huang et al., 2021).

The two-step improvement scheme—consisting of entropic mirror descent over the hinge loss surrogate, then regression-based policy matching—demonstrates that the essential mechanism for policy improvement is not the magnitude of $A$, but its direction. Thus, even with coarse or non-parametric advantage feedback, vanilla PPO loss is sufficient to drive global convergence at rate $O(1/\sqrt{T})$ under neural policy parameterizations. Theoretical bounds are unaffected by the clipping margin, and only the sign feedback is necessary for improvement, further mitigating the necessity for an explicit critic.

3. Off-Policy and Perceptron-Like Surrogates

By exploiting objective reformulations, critic-free PPO updates admit extensions into off-policy regimes. One formulation updates the policy whenever $A \cdot (\pi/\mu - 1) \leq 0$, where $\mu$ is the data-collection policy (Hu et al., 2019). In this setting, advantage direction is again the only required element; quantitative precision is less important.

Such perceptron-like objectives allow for arbitrarily distant $\pi$ and $\mu$, enabling robust off-policy training and combination with value correction schemes (e.g., V-trace from IMPALA). Experiments validate critic-free policy implementations on pendulum and quadrotor control tasks, achieving stable hovering and trajectory tracking solutions with real-time microcontroller deployment. This generalizes the applicability of vanilla PPO loss to embedded, real-world settings without heavy critic dependency.

4. Surrogate Variants and Regularization

Failures of standard PPO—arising from inadequate policy parameterizations, brittle clipping heuristics, or suboptimal surrogate objective selection—are largely remedied by principled surrogate regularization and alternative policy forms (Hsu et al., 2020).

KL-divergence regularization (forward or reverse) softens updates compared to hard ratio clipping. For example: $$L_{\text{forward}}(\theta) = \mathbb{E}[r(\theta) \hat{A}] - \beta D_{\text{KL}}(\pi_{\text{old}} \| \pi_\theta)$$ Moreover, Beta policies, defined for bounded spaces, allow uniform initial exploration, reducing convergence pathologies seen with Gaussian policies and softmax heads.

Empirical evidence shows that these design choices—whether in surrogate formulation or policy parameterization—improve stability and exploration in critic-free algorithms. Beta policy parameterizations more than doubled final cumulative rewards in select MuJoCo tasks, eliminating failure modes such as tail drift and local optimality near initialization.

5. Sample Efficiency, Partial Evaluation, and Mixed Losses

Modified Softened Policy Iteration (MoSoPI) further decouples policy improvement from value estimation by performing partial evaluation steps (repetitive BeLLMan applications, m-step TD regressions) and softened greedy improvements (PPO clipping) (Merdivan et al., 2019). MoPPO implementations yielded 5–10x sample efficiency gains over standard PPO, sometimes outperforming Soft Actor-Critic (SAC), underlining that critic-free PPO loss, when combined with partial evaluation and sample-efficient, off-policy updates, remains competitive.

Compound action losses—such as those encountered in complex games or multi-actuated robots—show that computing the PPO loss separately over sub-actions (rather than on the full joint action) dramatically improves sample efficiency in critic-free settings (Song et al., 2023). Mixed losses combining joint and sub-action perspectives further maximize information extraction per sample, yielding over 50% performance increases.

6. Early Stopping and Ratio-Free Regularization

Directly optimizing the original surrogate objective for multiple epochs—relying on early stopping conditions based on empirical ratio deviation metrics—allows vanilla PPO loss to perform robust critic-free learning without hard clipping (Sun et al., 2022).

The Early Stopping Policy Optimization (ESPO) algorithm halts updates once: $$E_{s,a \sim d_\pi} | (\tilde{\pi}(a|s)/\pi(a|s)) - 1 | > \delta$$ Experiments on Mujoco and DeepMind Control Suite demonstrate that ESPO outperforms clipped PPO, promoting steady improvement and resilient ratio control. Distributed variants preserve stability and scalability, all while maintaining the implementation simplicity characteristic of PPO.

7. Information-Geometric Foundations and Convergence Guarantees

Tighter surrogate analysis employing Fisher–Rao geometry establishes formal convergence results for PPO-style updates without explicit critic networks (Lascu et al., 4 Jun 2025). The core surrogate is: $$\Delta V \geq \frac{1}{1-\gamma} \left[\text{Advantage term}\right] - \frac{\| r \|}{2(1-\gamma)^3} \int_s FR^2(\pi'(\cdot|s)^2, \pi(\cdot|s)^2) d_{\rho}^{\pi}(s)$$ with monotonic improvement and sublinear convergence independent of state or action space dimensionality when the advantage is exactly known. This geometric perspective provides a justification for critic-free updates with vanilla PPO loss, as policy improvement is controlled via intrinsic distances rather than value estimation.

8. Value Estimation and Policy Gradient Robustness

Enhanced value estimation is found to be a critical lever for vanilla policy gradient (VPG) methods; increasing the number of value update steps per iteration enables VPG to achieve or surpass PPO performance (Wang et al., 25 May 2025). The regression loss: $$L_V(\phi) = \mathbb{E}_D [\| V(s;\phi) - V_{\text{target}}(s) \|^2 ]$$ when optimized repeatedly, yields accurate advantage estimates and robust learning, reducing hyperparameter sensitivity and obviating the need for additional trust-region enforcement.

9. Critic-Free PPO: Summary and Prospects

The synthesis of theoretical and empirical findings across these research directions demonstrates that the clipped surrogate mechanism, advantage sign dependence, soft regularization strategies (KL, hinge, or sigmoid functions), partial evaluation, and robust implementation pipelines together provide a foundation for critic-free policies using vanilla PPO loss. The critical properties underpinning these algorithms are:

• Stability and monotonicity via clipping or geometric penalty
• Sample efficiency through partial evaluation and repeated updates
• Reduced sensitivity to critic errors or hyperparameter mis-specification
• Maintained or enhanced exploration by preserving policy entropy

These attributes confirm that the complexity often associated with critic design and training can be substantially reduced without compromising performance on standard and real-world reinforcement learning tasks. Extensions into off-policy domains, distributed architectures, and high-dimensional action spaces further reinforce the practical flexibility of critic-free vanilla PPO approaches.

Plausible implications are that future research will explore ever more minimalist, robust actor-centric policy gradient methods based on these principles, offering scalable, theoretically grounded solutions for reinforcement learning applications requiring low variance and architectural simplicity.