Decentralized RL Experience Sharing

• Decentralized RL experience sharing is a methodology that allows autonomous agents to transfer, aggregate, and utilize learning experiences without centralized coordination.
• It employs techniques like selective transition exchange, policy aggregation, and imitation learning to reduce sample inefficiency and improve resilience in multi-agent systems.
• Practical implementations demonstrate significant gains in domains such as traffic control, cooperative robotics, and LLM post-training through robust, privacy-aware mechanisms.

Decentralized reinforcement learning (RL) experience sharing refers to a set of methodologies enabling multiple autonomous agents to transfer, aggregate, and utilize learning experiences (such as trajectories, transitions, parameters, or statistics) without centralized coordination or full global observability. This paradigm is motivated by scalability requirements, privacy and security constraints, communication limitations, and the realities of operating in dynamic, partially observable, or ad-hoc multi-agent environments.

1. Foundations and Motivations

Decentralized RL experience sharing aims to address inherent challenges in multi-agent learning—specifically, the non-stationarity of the environment as perceived by each agent due to concurrent policy updates, partial observability, privacy preservation, and the need for scalable knowledge transfer. Unlike classical centralized approaches, decentralized frameworks allow agents to maintain autonomy while selectively leveraging information from peers, typically over time- and context-limited communication interfaces.

The principal motivations include:

• Alleviation of sample inefficiency by enabling agents to benefit from the experiences and exploration conducted by others, thus accelerating convergence.
• Improved resilience to adversarial or faulty peers by robust aggregation or privacy-aware mechanisms.
• Feasibility in settings where centralized orchestration, full information sharing, or global communication are impractical due to bandwidth, hardware, or organizational constraints.

2. Mechanisms of Experience Sharing

Experience sharing in decentralized RL encompasses several interrelated mechanisms:

2.1 Direct Trajectory and Transition Exchange

Agents may exchange raw state-action-reward-next-state tuples with neighbors, as in protocols where replay buffers are selectively synchronized across a graph topology. Variants include:

• Sharing only a subset of transitions meeting a relevance condition, typically transitions with high temporal-difference (TD) error or those encompassing unexplored regions of state-space (Souza et al., 2019, Gerstgrasser et al., 2023, Dahal et al., 27 Jan 2025).
• Using prioritized experience relay: SUPER selects experiences for sharing by quantile, Gaussian tail, or stochastic weighting based on TD error to maximize learning impact while constraining communication bandwidth (Gerstgrasser et al., 2023).

2.2 Model, Policy, or Statistic Aggregation

Instead of sharing individual samples, agents may:

• Aggregate distilled knowledge (e.g., proxy policies, time-averaged action distributions over clustered or proxy states), thereby obfuscating detailed trajectories for privacy (Cha et al., 2019).
• Conduct parameter or value function consensus steps, often employing gossip-style averaging or consensus matrices across a dynamically connected peer network (Cheruiyot et al., 8 Jul 2025, Li et al., 2021).

2.3 Centralized Training, Decentralized Execution (CTDE) and Imitation Learning

A prevalent method is to first solve the multi-agent problem centrally—using a joint policy over the full observation/action space—then transfer distilled policy knowledge to decentralized agents through supervised imitation learning (e.g., DAgger variants). The CESMA algorithm exemplifies this by decomposing the joint expert's decisions across agents and minimizing supervised loss with respect to expert action labels, yielding theoretical performance guarantees (Lin et al., 2019). Communication protocols may supplement learning where local information is insufficient.

2.4 Privacy and Security-Aware Sharing

Experience exchange may be constrained by privacy requirements or the presence of adversarial agents. For example:

• Federated reinforcement distillation (FRD) enables privacy-preserving sharing by clustering the state space, exchanging only aggregated policy outputs per cluster, thus concealing individual trajectories (Cha et al., 2019).
• BRNES applies local differential privacy via generalized randomized response to each Q-value before sharing, dynamically adjusts neighbor zones, and uses weighted aggregation to mitigate Byzantine attacks (Hossain et al., 2023).

2.5 Asynchronous, Fully Decentralized Sampling

Modern large-scale systems (e.g., for RL-based LLM post-training or swarm robotics) employ asynchronous decentralized sampling and sharing of rollouts. SAPO allows nodes to independently generate and share (plain text) rollouts, enabling "Aha moments" to propagate quickly within the swarm (Amico et al., 10 Sep 2025). Reliability is enhanced as nodes maintain autonomy and can operate in isolation or partial connectivity.

3. Communication Topologies and Protocols

Experience sharing efficiency critically depends on the underlying communication graph and exchange protocol:

• Static topologies such as fully-connected, ring, and small-world networks exhibit differing trade-offs in exploration, diversity maintenance, and convergence. Dynamic or time-varying topologies, where neighbor sets change periodically, can combine periods of isolated exploration with rapid dissemination of beneficial experiences, thereby preventing premature convergence on local optima and supporting innovation (Nisioti et al., 2022).
• Message-passing protocols may include basic neighbor broadcast, γ-hop relaying (restricting message lifetime and thus reach), or full diffusion by consensus steps (Lidard et al., 2021, Cheruiyot et al., 8 Jul 2025).
• Synchronization can be managed by explicit versioning (as in Echo’s param_version tags), sequential pulls for bias minimization, or asynchronous push–pull buffers for maximized hardware utilization in heterogeneous compute environments (Xiao et al., 7 Aug 2025).

4. Theoretical Guarantees and Performance Bounds

Several frameworks provide explicit performance analysis:

• CESMA guarantees that if the decentralized agents maintain a supervised loss μₙ relative to a centralized expert, the cumulative reward satisfies

$$R(\hat{\pi}_1, \ldots, \hat{\pi}_M) \ge R(\pi^*) - uT\mu_N - O(1),$$

where $u$ is the maximum per-step expert deviation cost and $T$ is the horizon (Lin et al., 2019).

• Consensus-based actor-critic methods converge to a common value estimator (for fixed policies) under column-stochastic weighting and joint connectivity assumptions, provided that function approximation is linear and updates split into two time scales (Cheruiyot et al., 8 Jul 2025).
• In tabular decentralized Q-learning with message-passing, the group regret scales as $O(\sqrt{M H^4 S A T \log(SATM/p)})$ and the per-agent sample complexity improves with $M^{-1/2}$ relative to naïve parallelization (Lidard et al., 2021).
• Decentralized multi-task frameworks (DistMT-LSVI) achieve a sample complexity reduction of $1/N$ using centralized policy/statistic sharing:

$\tilde{O}(d^3 H^6 M \epsilon^{-2} / N),$

for $N$ agents jointly solving $M$ tasks with a linearly parameterized contextual MDP model (Amani et al., 2023).

5. Applications and Empirical Results

Decentralized RL experience sharing has been empirically validated in diverse domains:

• Traffic signal control agents employing state/reward sharing reduced vehicle delay by up to 34.55% and queue lengths by 10.91% over local baselines while preserving generalization (Guo, 2020).
• In cooperative control tasks, time-dependent prioritization and simulated imaginary experiences improved credit assignment and coordination, with formalized accountability-driven update schedules (Köpf et al., 2019).
• Selective experience sharing (e.g., based on TD-error thresholds) yielded an average 51% reduction in sample complexity for reaching completion in CartPole benchmarks, with focused or prioritized variants outperforming uniform or naïve sharing (Souza et al., 2019).
• Application-specific frameworks, such as interference management in base station networks, achieved 98% of the spectral efficiency of full sharing with only 25% of the communication bandwidth by sharing only high-interference experiences (Dahal et al., 27 Jan 2025).
• In large-scale LLM post-training, asynchronous decentralized sharing of rollouts (as in SAPO) enabled up to 94% cumulative reward improvement, with scalability over thousands of heterogeneous nodes demonstrated in open-source testnets (Amico et al., 10 Sep 2025).

6. Practical Considerations and Limitations

Key practical aspects governing the success and generalizability of decentralized experience sharing capacity include:

• Hyperparameter tuning: The effectiveness of selective sharing regimes (e.g., proportion β for TD-error selection) is robust to a range of settings, but too aggressive sharing can dilute diversity, cause oscillations, or lead to premature convergence (Gerstgrasser et al., 2023, Amico et al., 10 Sep 2025).
• Scalability and communication overhead: Selective and proxy-based sharing approaches are essential for scaling to large networks and constrained communication environments; aggressive full sharing is generally infeasible beyond small numbers of agents and risks redundancy and privacy erosion (Cha et al., 2019, Amani et al., 2023).
• Security and privacy: By integrating local differential privacy, dynamic neighbor selection, and robust aggregation schemes, agents can protect performance from both adversarial manipulation (Byzantine faults) and inference attacks (Hossain et al., 2023).
• Policy and state compatibility: Many experience sharing methods assume homogeneous state-action spaces and compatible policy representations. Extensions to heterogeneous, dynamic, or non-stationary settings remain an open area of active research (Souza et al., 2019, Mishra et al., 2020, Du et al., 26 Jan 2025).
• Theoretical performance: Results are generally strongest in tabular or linearly approximated settings, with guarantees linked to consensus rates, surrogate imitation loss, or model separability. Fully practical, theoretically grounded methods for function approximation and deep RL remain an open problem (Cheruiyot et al., 8 Jul 2025).

7. Directions for Future Research

The surveyed literature identifies several promising research avenues:

• Relaxing homogeneity assumptions to enable experience transfer among agents differing in network architectures, observation spaces, or action sets (Souza et al., 2019).
• Extending decentralized sharing mechanisms to adversarial and mixed-cooperative-competitive Markov games (Souza et al., 2019).
• Developing adaptive and context-sensitive dynamic topologies for connectivity and exchange, enabling a trade-off between diversity maintenance and rapid information propagation (Nisioti et al., 2022).
• Investigating advanced privacy-preserving aggregation, encrypted parameter exchange, or adaptive trusted advisor schemes to address real-world deployment challenges (Cha et al., 2019, Hossain et al., 2023).
• Scaling theory and practice for deep function approximation, large action spaces, and non-stationary tasks where model consensus and synchronization become more complex (Xiao et al., 7 Aug 2025, Amico et al., 10 Sep 2025).

Summary

Decentralized RL experience sharing encompasses a broad set of algorithmic strategies, including selective and prioritized sample exchange, proxy and aggregate knowledge distillation, decentralized imitation, privacy-aware and robust aggregation, and asynchronous collective adaptation. Empirical and theoretical results consistently demonstrate that judicious sharing of high-impact experiences—or well-structured policy/value summaries—accelerates distributed learning, improves efficiency and resilience, and opens new avenues for large-scale, robust, and privacy-preserving multi-agent RL in complex domains. The field continues to evolve toward integrating richer communication protocols, advanced incentive-compatible sharing mechanisms, and robust, efficient execution in the face of real-world constraints.