A new method for RL with continuous actions improves stability and scalability.
problem Stability and scalability issues in existing RL methods.
method Soft policy gradient with entropy regularization, combined with double sampling for soft Bellman equation.
result Outperforms off-policy prior methods in continuous action RL tasks.
MSOL learns hierarchical policies for multitask tasks with soft options.
problem Training hierarchical policies for multiple tasks with stability and flexibility.
method MSOL uses separate variational posteriors for each task, regularized by a shared prior, to avoid instabilities and fine-tune options for new tasks.
result MSOL significantly outperforms hierarchical and flat transfer-learning baselines.
New method improves stability of soft FQI for offline RL.
problem Stability issues in soft FQI under function approximation.
method Stationary reweighting to align operator norms.
result Local linear convergence proved under certain conditions.
Combines soft greediness with Modified Policy Iteration for more efficient deep reinforcement learning.
problem Improving sample efficiency in deep reinforcement learning.
method Combines soft greediness with Modified Policy Iteration (MPI) for off-policy learning.
result The proposed algorithm is more sample efficient than original PPO and competitive with SAC.
Soft Q-learning improves sample efficiency in robotic manipulation.
problem Limited interaction time in real-world robotic tasks.
method Soft Q-learning for maximum entropy policies, with composability.
result Soft Q-learning policies are more sample efficient and can be composed.
New reinforcement learning algorithms improve policy optimization with entropy regularization.
problem Improving policy optimization in reinforcement learning.
method Soft policy gradient theorem (SPGT) and new policy optimization algorithms.
result New algorithms outperform prior works on various benchmark tasks.
Short note on soft-max and policy gradients in bandit problems using Lyapunov functions.
problem Analyzing soft-max and policy gradient methods in bandit problems.
method Lyapunov function argument for soft-max and differential equations for policy gradient algorithms.
result Regret bounds for soft-max and a different policy gradient algorithm in bandit problems.
New algorithm learns robust policies without being overly conservative.
problem Learning robust policies can be overly conservative.
method Soft-Robust Actor-Critic (SR-AC) algorithm that considers a distribution over uncertainty sets.
result SR-AC avoids the conservativeness of robust strategies while maintaining robustness.
Paper proposes a pretraining method for soft Q-learning from imperfect demonstrations.
problem Challenges in exploiting expert demonstrations while maintaining exploration potentials.
method γ-discounted biased policy evaluation with entropy regularization.
result Our method effectively learns from imperfect demonstrations and outperforms other methods.
New algorithms optimize a soft-robust criterion in reinforcement learning, reducing conservatism.
problem Computing robust policies for high-stakes decisions with limited data.
method Soft-robust criterion using risk measures, two algorithms for optimization.
result Our algorithms produce less conservative solutions than existing methods.
LC-SAC tackles non-stationary dynamics in reinforcement learning.
problem Degradation of deep RL methods in non-stationary environments.
method LC-SAC uses latent context encoders and contrastive loss for dynamic information capture.
result LC-SAC outperforms SAC on environments with drastic dynamics changes.
Improved exploration in SAC using Normalizing Flows policies.
problem Brittleness and inefficiency of DRL algorithms in continuous action spaces.
method Introducing Normalizing Flow policies within the SAC framework to learn more expressive policies.
result Increased stability and better exploration in sparse reward settings.
Soft modularization improves sample efficiency and performance in reinforcement learning.
problem Challenges in training multiple tasks jointly in reinforcement learning.
method Explicit modularization technique on policy representation, soft modularization method.
result Improves sample efficiency and performance over strong baselines in robotics manipulation tasks.
New algorithms estimate Q-functions under partial coverage and realizability, improving offline RL guarantees.
problem Offline RL with limited exploration and assumptions about data coverage and Q-function realizability.
method Proposes minimax learning algorithms to estimate soft or vanilla Q-functions with L2-convergence guarantees. result PAC guarantees for offline RL under partial coverage and realizability conditions.
Sparse PCL algorithms improve optimal policy in Tsallis entropy-regularized MDPs.
problem Sparse optimal policies in Tsallis entropy-regularized MDPs.
method Path consistency learning (PCL) algorithms for sparse entropy-regularized RL.
result Sparse PCL algorithms reduce sub-optimality compared to soft ERL, especially in high-action problems.
Improved off-policy reinforcement learning by discounting and soft normalization.
problem Divergence issues in off-policy reinforcement learning.
method Introducing a discount factor and a soft normalization penalty into COP-TD.
result Discounted COP-TD is better behaved both theoretically and empirically.
New algorithms improve reinforcement learning with multi-step greedy policies.
problem Difficulty in monotonic policy improvement with soft-policy updates.
method Formulated and analyzed online and approximate algorithms using multi-step greedy operators.
result Guaranteed monotonic policy improvement with sufficiently large update stepsize.
Soft Actor-Critic improves deep RL with entropy maximization.
problem High sample complexity and brittle convergence in deep RL.
method Maximum entropy reinforcement learning framework, off-policy updates, stochastic actor-critic.
result State-of-the-art performance on continuous control tasks.
Paper tackles DOCTR-L with SciPhy RL, solving neural PDEs from data.
problem High-dimensional optimal control with stochastic policies.
method Soft HJB equation, Neural PDEs, Physics-Informed Neural Networks.
result Reduces DOCTR-L to solving neural PDEs from data.
Improves RL generalization by minimizing adversarial risk.
problem Overfitting to training environments and poor generalization to unseen scenarios.
method Introduces minimax formulation and distributional framework to RL.
result Trained policy shows improved generalization to different environments.
New method combines value function decomposition and policy gradients for cooperative multi-agent reinforcement learning.
problem Challenges in cooperative multi-agent reinforcement learning, especially credit assignment and large action spaces.
method Decomposed Soft Actor-Critic (mSAC) method with Q network architecture, discrete probabilistic policy, and counterfactual advantage function.
result Significantly outperforms policy-based approach COMA and achieves competitive results with SOTA value-based approach Qmix.
Paper introduces RCaI, a risk-sensitive control method using Rényi divergence.
problem Risk-sensitive control in reinforcement learning.
method RCaI extends CaI using Rényi divergence variational inference.
result Risk-sensitive optimal policy can be obtained by solving a soft Bellman equation.
The paper uses potential functions to help reinforcement learning agents learn optimal policies.
problem Learning optimal stochastic policies in reinforcement learning.
method Augmenting the reward with potential functions and applying these to policy gradient algorithms.
result Potential-based reward shaping preserves optimality of stochastic policies and speeds up learning.
CSAC enables cooperative reinforcement learning for multi-stage tasks.
problem Coordinating consecutive reinforcement learning agents for long-term multi-stage tasks.
method CSAC modifies each agent's policy to maximize both current and next agent's critic.
result CSAC outperforms uncooperative policies and single-agent training in multi-room maze domain.
Enhances RL performance with a population-guided parallel learning scheme.
problem Improving off-policy reinforcement learning performance.
method Population-guided parallel learning scheme with shared experience replay buffer and soft policy update.
result Monotone improvement of the expected cumulative return proved theoretically and demonstrated in practice.
Improved SAC with AWMP for better control tasks.
problem Discontinuous and non-smooth optimal policies in reinforcement learning.
method Advantage Weighted Mixture Policy (AWMP) for SAC, learning state-specific weights.
result SAC with AWMP outperforms SAC in four control tasks.
A new method removes policy optimization in adversarial imitation learning.
problem Adversarial imitation learning's delicate alternated optimization.
method Explicitly condition discriminator on two policies, solving generator's optimization problem directly.
result Simpler approach competitive to prevalent methods.
Proposes a new sampling policy for ranking and selection problems.
problem Improving ranking and selection in adaptive sampling policies.
method Annealed entropic allocation, using soft-min weights and saddlepoint corrections.
result Consistently competitive performance in various settings.
MODULE solves LfO problem with high sample efficiency and stability.
problem Sample inefficiency and instability in LfO.
method Modified DSAC with MODULE algorithm.
result Superior performance in MuJoCo environments.
Enhances SAC for better sample efficiency in continuous-action tasks.
problem Improving sample efficiency in soft actor-critic algorithms.
method Integrating Emphasizing Recent Experience (ERE) with Soft Actor-Critic (SAC) and Priority Experience Replay (PER).
result ERE significantly improves sample efficiency compared to vanilla SAC, especially for continuous-action tasks.
SAC-NF improves RL exploration by using normalizing flows to discover policies faster.
problem Discovering optimal policies in sparse reward domains.
method Extending SAC with normalizing flows to improve exploration efficiency.
result SAC-NF accelerates policy discovery with smaller parameter usage.
Paper improves AIRL by enhancing policy imitation and addressing reward recovery issues.
problem Inadequate policy imitation and limited transferable reward recovery in AIRL.
method Substituted built-in algorithm with SAC for policy updating and proposed PPO-AIRL + SAC hybrid framework.
result SAC improves policy imitation but hinders reward recovery; PPO-AIRL + SAC achieves satisfactory transfer effect.
Paper analyzes convergence of entropy-regularized reinforcement learning methods.
problem Ensuring sub-optimality control in entropy-regularized RL.
method Unified analysis of regularized MPI and VI schemes, providing convergence rates.
result Established sufficient conditions for convergence and provided explicit rates.
GSI improves efficiency of large language model inference.
problem Efficiently guiding test-time alignment in large language models.
method Combines soft best-of-n scaling with a reward model and speculative samples. result Achieves higher accuracy and reduced latency compared to standard methods.
A new policy improvement method using CEM for Actor-Critic.
problem Improving policy efficiency and robustness in reinforcement learning.
method Greedy Actor-Critic (Greedy AC) using Conditional Cross-Entropy Method (CCEM).
result Greedy AC outperforms Soft Actor-Critic and is less sensitive to entropy regularization.
SAC improves deep RL by balancing reward and randomness.
problem High sample complexity and hyperparameter brittleness in RL.
method Maximum entropy framework, constrained optimization, temperature tuning.
result SAC achieves state-of-the-art performance and stability.
Paper uses RL to optimize multi-asset portfolios in fluctuating markets.
problem Optimizing multi-asset portfolios in time-varying financial markets.
method Soft Actor-Critic (SAC) algorithm for policy learning, policy iteration process.
result SAC algorithm outperforms in various criteria in simulated and real financial markets.
A new method stabilizes deep reinforcement learning by using QGraphs to retain replay memory information.
problem Stabilizing model-free off-policy deep reinforcement learning with soft divergence.
method Representing past experiences as a QGraph, selecting a subgraph with favorable structure, and using lower bounds for temporal difference learning.
result QG-DDPG method is less prone to soft divergence and more robust to hyperparameters.
New reinforcement learning bound improves generalization for sequential data.
problem Challenges in obtaining generalization guarantees for reinforcement learning due to sequential data.
method PAC-Bayesian reinforcement learning with consideration of Markov dependencies and mixing time.
result Demonstrated practical utility through PB-SAC, providing meaningful confidence certificates.
Improved inference-time alignment using Best-of-N and smoothing.
problem Reward overoptimization in Best-of-N (BoN) due to poor proxy reward models.
method Introduced Soft Best-of-N (SBoN) and analyzed its performance through KL divergence and regret analysis.
result Smoothing helps SBoN mitigate reward overoptimization, especially when proxy reward quality is low.
Paper proposes risk-averse reinforcement learning algorithms.
problem Managing model uncertainty in reinforcement learning.
method Entropic risk constrained policy gradient and actor-critic algorithms.
result Demonstrates usefulness of risk-averse algorithms on various domains.
Simple model-based reinforcement learning outperforms model-free methods in complex tasks.
problem Lagging performance of model-based reinforcement learning agents in non-trivial environments.
method Combining soft value estimates with stochastic value gradients.
result Simple model-based agents achieve state-of-the-art results in a high-dimensional humanoid control task.
Paper improves RL from imperfect demonstrations with soft expert guidance.
problem Improper and insufficient expert demonstrations in RLfD.
method Formalizes imperfect expert setting, tackles optimality and convergence issues with soft constraints, and uses local linear search on dual form.
result Method achieves consistent improvement over other RLfD methods.
New PAC-Bayesian approach stabilizes actor-critic learning.
problem Training instability in actor-critic algorithms.
method Employing PAC-Bayesian bound as the critic training objective.
result Significant improvement in online learning performance.
New approach transfers rewards learned in one environment to reinforcement learning in a new environment.
problem Transfer of rewards learned using inverse reinforcement learning from one environment to a new, different environment.
method Formulate the problem as a joint system of Bellman equations, develop minimax estimators for the target soft-q-function, solve the source and target system of equations jointly. result The coupled approach removes the first-order influence of source Bellman residual error compared to the sequential approach.
OTSS learns personalized decision weights from logged decisions and outputs.
problem Learning context-specific decision weights from logged decisions and outputs.
method Output-targeted soft-segmentation model that deploys personalized decision-ready weight vectors.
result OTSS achieves the lowest mean regret in benchmark settings.
Optimizes exploration in networks by interpolating between random and deterministic paths.
problem Balancing exploitation and exploration in network routing with constraints.
method Developed a constrained randomized shortest-paths framework using Lagrangian duality and iterative procedures.
result Optimal routing policy that interpolates between random and deterministic paths while satisfying constraints.
Framework for robust RL in continuous control with model misspecification.
problem Model misspecification in reinforcement learning for continuous control.
method Integrates robustness into MPO algorithm through worst-case expected return objective and entropy regularization.
result Robust and soft-robust policies outperform non-robust policies in various domains.