MushroomRL simplifies RL experiments for researchers.
problem Complexity in implementing and testing RL experiments.
method Provides a comprehensive and flexible framework to minimize effort.
result Significantly benefits RL researchers in empirical analysis.
Rewriting history improves RL algorithms for solving multiple tasks.
problem Improving sample efficiency in multi-task reinforcement learning.
method Introducing hindsight relabeling as inverse RL to generalize goal-relabeling techniques.
result Relabeling data using inverse RL accelerates learning in multi-task settings.
New method for RL tasks transfer using Lipschitz continuity.
problem Knowledge transfer in RL tasks over time.
method Established Lipschitz continuity between MDPs and applied it to RL.
result Improved convergence rate and no negative transfer with high probability.
SynthER uses generative models to augment limited RL experience.
problem Limited data for reinforcement learning agents.
method SynthER leverages diffusion models to generate synthetic experience data.
result SynthER significantly improves sample efficiency and training of RL agents.
New method improves meta-reinforcement learning efficiency.
problem Sample inefficiency in meta-reinforcement learning.
method Hindsight Foresight Relabeling (HFR) method.
result HFR improves performance on various meta-reinforcement learning tasks.
Deep RL algorithms can overfit to early experiences, leading to poor performance.
problem Overfitting to early interactions in deep reinforcement learning.
method Proposed a mechanism to periodically reset part of the agent to mitigate overfitting.
result Periodic resetting improves performance in both discrete and continuous action domains.
Transformer RL optimizes A/B testing for time series experiments.
problem Challenges in applying A/B testing to time series experiments, especially with limited history and strong assumptions.
method Transformer reinforcement learning approach that conditions allocation on full history and optimizes MSE without restrictive assumptions.
result Consistently outperforms existing designs in synthetic, simulator, and real-world data.
Deep reinforcement learning algorithms require large amounts of experience to learn an individual task. While in principle meta-reinforcement learning (meta-RL) algorithms enable agents to learn new skills from small amounts of experience, several major challenges preclude their practicality. Current methods rely heavi…
New unsupervised learning task improves RL performance.
problem Reward-driven feature learning limitations in RL from images.
method Introduce Augmented Temporal Contrast (ATC) for unsupervised learning of image representations.
result Training encoders using ATC matches or outperforms end-to-end RL in most environments.
New insights into experience replay in RL algorithms.
problem Understanding the impact of replay capacity and replay ratio in Q-learning.
method Systematic and extensive analysis of experience replay in Q-learning methods, focusing on replay capacity and replay ratio.
result Greater replay capacity significantly improves performance for certain algorithms, while other techniques offer limited benefit.
RADIAL-RL improves deep RL agents' robustness against adversarial attacks.
problem Vulnerability of deep reinforcement learning agents to small adversarial perturbations.
method RADIAL-RL, a principled framework for training robust reinforcement learning agents.
result RADIAL-RL-trained agents consistently outperform prior methods in robustness tests.
Catalyst.RL accelerates RL research with efficient training.
problem Efficient reinforcement learning training in complex environments.
method Open-source PyTorch framework with distributed training and RL algorithms.
result Catalyst.RL achieved 2nd place in a computationally expensive RL challenge.
New method defends RL agents from poisoning attacks without MDP knowledge.
problem Poisoning attacks on RL systems can cause learning failures.
method Generic poisoning framework for online RL, Vulnerability-Aware Adversarial Critic Poison (VA2C-P).
result Successfully prevents RL agents from learning good policies or converging to target policies.
Most meta reinforcement learning (meta-RL) methods learn to adapt to new tasks by directly optimizing the parameters of policies over primitive action space. Such algorithms work well in tasks with relatively slight difference. However, when the task distribution becomes wider, it would be quite inefficient to directly…
D2D-SPL uses discrete states and a classifier to train RL faster.
problem Training neural networks in RL due to correlated samples.
method Discretizes state space, uses actor-critic, selects input/target pairs, trains classifier.
result Trains faster than state-of-the-art methods.
Unified framework for reliable uncertainty quantification in RL.
problem Uncertainty quantification in high-stakes reinforcement learning.
method Unified conformal prediction framework integrating distributional RL and conformal calibration.
result Significantly improved coverage and reliability over standard methods.
Experience replay (ER) is a fundamental component of off-policy deep reinforcement learning (RL). ER recalls experiences from past iterations to compute gradient estimates for the current policy, increasing data-efficiency. However, the accuracy of such updates may deteriorate when the policy diverges from past behavio…
This paper proposes a cascading failure mitigation strategy based on Reinforcement Learning (RL) method. Firstly, the principles of RL are introduced. Then, the Multi-Stage Cascading Failure (MSCF) problem is presented and its challenges are investigated. The problem is then tackled by the RL based on DC-OPF (Optimal P…
Sentiment analysis from LLMs improves financial trading performance.
problem Improving dynamic strategy optimization in financial markets.
method Integration of sentiment analysis from LLMs into RL frameworks.
result Sentiment-enhanced RL models outperform traditional RL models in net worth and cumulative profit.
Despite the recent progress in deep reinforcement learning field (RL), and, arguably because of it, a large body of work remains to be done in reproducing and carefully comparing different RL algorithms. We present catalyst.RL, an open source framework for RL research with a focus on reproducibility and flexibility. Ma…
MOReL learns offline RL policies using pessimistic MDPs.
problem Offline RL's data efficiency and velocity.
method Two-step process: learn P-MDP and near-optimal policy in it.
result MOReL is minimax optimal and matches state-of-the-art results.
MiniHack simplifies creation of complex RL environments.
problem Limited availability of challenging RL benchmarks.
method Develops a sandbox framework for easy RL environment design.
result MiniHack enables rapid creation of diverse RL testbeds.
We introduce Threatened Markov Decision Processes (TMDPs) as an extension of the classical Markov Decision Process framework for Reinforcement Learning (RL). TMDPs allow suporting a decision maker against potential opponents in a RL context. We also propose a level-k thinking scheme resulting in a novel learning approa…
Conservative Policy Iteration (CPI) is a founding algorithm of Approximate Dynamic Programming (ADP). Its core principle is to stabilize greediness through stochastic mixtures of consecutive policies. It comes with strong theoretical guarantees, and inspired approaches in deep Reinforcement Learning (RL). However, CPI …
Surrogate models speed up RL training in dynamic systems.
problem High computational cost of high-fidelity simulations.
method Developed and tested surrogate models for RL training.
result Surrogate models can significantly accelerate RL training.
New RL method improves robustness across various conditions.
problem Training robust RL agents for diverse environments.
method Adversarial training with Langevin Dynamics.
result Consistently outperforms existing RL algorithms in generalization.
A3RL combines online and offline RL with active sampling to improve policy learning.
problem Combining online and offline RL for sample efficiency and robustness.
method A3RL uses a confidence-aware Active Advantage Aligned (A3) sampling strategy to prioritize data from both online and offline sources.
result A3RL outperforms competing online RL techniques that use offline data.
SLM Lab is a framework for reproducible RL research with modular algorithms.
problem Reproducibility in deep reinforcement learning.
method Modular software framework for RL algorithms, synchronous/asynchronous execution, hyperparameter search, result analysis.
result Comprehensive benchmark and novel RL algorithms (e.g., discrete-AC variant, hybrid training method).
Novel meta-RL strategy improves efficiency in learning novel tasks.
problem Efficiency in learning novel tasks using deep RL.
method Decomposes meta-RL into task-exploration, task-inference, and task-fulfillment; uses deep networks and a task encoder.
result Improves sample efficiency and mitigates meta-overfitting.
This work bridges offline RL and DRL to address distributional shift.
problem Distributional shift in offline RL due to difference in state-action visitation distributions.
method Proposes offline RL algorithms using DRL framework, characterizes sample complexity under single policy concentrability.
result Demonstrates superior performance of proposed algorithms through simulations.
We present a modern scalable reinforcement learning agent called SEED (Scalable, Efficient Deep-RL). By effectively utilizing modern accelerators, we show that it is not only possible to train on millions of frames per second but also to lower the cost of experiments compared to current methods. We achieve this with a …
Deep RL agents suffer from transient non-stationarity, which ITER mitigates.
problem Transient non-stationarity in deep RL agents affects generalization.
method Iterated Relearning (ITER) transfers knowledge between networks to reduce non-stationarity.
result ITER improves deep RL agents' performance on generalization benchmarks.
Reinforcement Learning (RL) aims at learning an optimal behavior policy from its own experiments and not rule-based control methods. However, there is no RL algorithm yet capable of handling a task as difficult as urban driving. We present a novel technique, coined implicit affordances, to effectively leverage RL for u…
This work uses RL to optimize batch experiments in SDOE.
problem Maximizing knowledge with limited resources and constraints.
method Reinforcement Learning for batch-sampling in Bayesian SDOE.
result The proposed algorithm optimizes experiment selection for multiple tasks.
RL optimizes resource allocation in MG by balancing experience and exploration.
problem Optimal resource allocation in competitive scenarios.
method Introduced RL to MG, allowing dynamic strategy adjustment based on experience and expected rewards.
result Achieves optimal resource coordination by balancing exploitation and exploration.
NetHack Learning Environment (NLE) tests RL algorithms, offering scalable, complex, and challenging gameplay.
problem Challenging environments for testing RL algorithms.
method Procedurally generated, stochastic, rich, and complex NetHack environment.
result Demonstrates empirical success for early stages of NetHack using RL.
CARL safely adapts RL agents for safety-critical tasks.
problem Safety hazards in RL for safety-critical tasks.
method CARL combines model-based RL and cautious adaptation.
result CARL achieves higher rewards with fewer failures in safety-critical tasks.
In distributed reinforcement learning, it is common to exchange the experience memory of each agent and thereby collectively train their local models. The experience memory, however, contains all the preceding state observations and their corresponding policies of the host agent, which may violate the privacy of the ag…
We explore the impact of learning paradigms on training deep neural networks for the Travelling Salesman Problem. We design controlled experiments to train supervised learning (SL) and reinforcement learning (RL) models on fixed graph sizes up to 100 nodes, and evaluate them on variable sized graphs up to 500 nodes. Be…
SIBRE boosts reinforcement learning convergence by rewarding improvement over past performance.
problem Improving the rate of convergence in reinforcement learning.
method SIBRE is a reward shaping approach that rewards improvement over the agent's own past performance.
result SIBRE converges faster and more stably to the optimal policy compared to baseline RL algorithms.
Improves sample efficiency and generalization in vision-based RL by enhancing exploration.
problem Low sample efficiency in vision-based RL using images as observations.
method Integrates state representation learning to enhance exploration and sample diversity.
result Significant improvement in sample efficiency across various environments.
Reinforcement learning (RL) algorithms have made huge progress in recent years by leveraging the power of deep neural networks (DNN). Despite the success, deep RL algorithms are known to be sample inefficient, often requiring many rounds of interaction with the environments to obtain satisfactory performance. Recently,…
RL Unplugged benchmarks offline RL methods across diverse domains.
problem Evaluate offline reinforcement learning methods without online data collection.
method Proposes a benchmark suite with diverse datasets and detailed evaluation protocols.
result Demonstrates the effectiveness of offline RL methods across various domains.
Paper tackles action selection in deep RL, proposing a data-driven approach.
problem High-dimensional action selection in deep RL environments.
method Data-driven approach with knockoff sampling for minimal sufficient actions.
result Method surpasses alternative techniques in performance and rewards.
RL enhances cryptocurrency trading profits.
problem Enhancing cryptocurrency trading profits through dynamic scaling.
method Combining RL with pair trading, using new reward shaping and observation/action spaces.
result RL-based trading achieved 9.94% to 31.53% annualized profits, vs. 8.33% for traditional methods.
In recent years deep reinforcement learning (RL) systems have attained superhuman performance in a number of challenging task domains. However, a major limitation of such applications is their demand for massive amounts of training data. A critical present objective is thus to develop deep RL methods that can adapt rap…
Although reinforcement learning (RL) can provide reliable solutions in many settings, practitioners are often wary of the discrepancies between the RL solution and their status quo procedures. Therefore, they may be reluctant to adapt to the novel way of executing tasks proposed by RL. On the other hand, many real-worl…
New RL algorithm learns good actions from offline data, reducing uncertainty and divergence.
problem Limited applicability of current RL algorithms in real-world settings due to high costs of exploration.
method Proposes an algorithm for batch RL using a fixed offline dataset, with penalties for policy and value constraints.
result Compared favorably to state-of-the-art methods on 32 continuous-action benchmarks.