Proposes ABIDES-Gym for financial markets simulation.
problem Training multi-agent systems in financial markets.
method Wrapping DEMAS into OpenAI Gym framework.
result Developed two financial markets OpenAI Gym environments.
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48 results for “OpenAI gym”
GAN Q-learningstat.ML
GAN Q-learning uses GANs for distributional RL in tabular and gym environments.
problem Complex MDPs in nonlinear function approximation.
method Generative adversarial networks (GANs) for distributional reinforcement learning.
result Empirically shows GAN Q-learning is a viable alternative to traditional methods.
Photonic quantum reinforcement learning for control problems.
problem Solving continuous control problems with noisy quantum computers.
method Proximal policy optimization for photonic variational quantum agents.
result Photonic policy learning achieves comparable performance to classical neural networks.
Multi-agent Q-learning untangles braids, improving over training.
problem Untangling braids using reinforcement learning.
method Two competing players learn to tangle and untangle braids in the OpenAI Gym environment.
result The system improves at untangling braids with more training, producing good tangled examples.
Online random forests improve Q-learning performance in specific gym environments.
problem Improving Q-learning performance in reinforcement learning tasks.
method Proposed online random forests as Q-function approximators and growing them as learning progresses.
result Improved performance over state-of-the-art Deep Q-Networks in specific gym environments.
Combines experience replay techniques to improve reinforcement learning.
problem Improving reinforcement learning algorithms.
method Combines CER, PER, and HER with DDPG and DQN.
result Effective combinations of these techniques in various environments.
Tool converts industrial systems to RL environments for optimization.
problem Lack of RL methods for optimizing industrial systems.
method Develops a tool to convert FSM models to OpenAI Gym wrappers.
result Traditional RL methods can be applied to industrial systems.
A simple DQN-based multi-agent RL system for binary actions.
problem Complexity and training issues in multi-agent reinforcement learning.
method Shared state and rewards, agent-specific actions, experience replay pool.
result Better performance and faster convergence compared to conventional methods.
Simulates sepsis treatment decisions using a world model approach.
problem Predicting optimal sepsis treatment actions based on noisy EHR data.
method Uses a Variational Auto-Encoder and Mixture Density Network (MDN-RNN) to model sepsis patient trajectories.
result Simulator learns from MIMIC dataset to predict patient states.
New method uses feedback to improve deep reinforcement learning efficiency.
problem Vast amounts of data needed for reasonable performance in deep reinforcement learning.
method Binary corrective feedback combined with probabilistic conditional exploration.
result Achieves drastic improvements in sample efficiency and robustness.
Deep RL algorithms tested on diverse video games.
problem Benchmarking AI on diverse video games.
method Interface GVGAI to OpenAI Gym, test deep RL algorithms.
result Characterized performance of deep RL algorithms on GVGAI games.
RAD enhances RL algorithms with data augmentations.
problem Challenges in RL learning from visual observations.
method RAD is a simple plug-and-play module for RL algorithms.
result RAD improves data-efficiency and final performance.
Provably Efficient Convergence of Primal-Dual Actor-Critic with Nonlinear Function Approximationcs.LG
Study efficient convergence of RL algorithm with function approximation.
problem Convergence of actor-critic algorithm with nonlinear function approximation.
method Stochastic gradient descent ascent with adaptive proximal term, Polyak-Łojasiewicz condition.
result First efficient convergence result with rate of O(sqrt{ln(N d G^2) / N}).
New algorithm learns Gaussian policies from corrective human feedback, outperforming current methods.
problem Learning from corrective human feedback for complex systems.
method Gaussian Process Coach (GPC) that uses Gaussian Processes and policy uncertainty for optimal feedback selection and learning rate adaptation.
result Demonstrated superior performance in OpenAI Gym benchmarks compared to COACH.
MobILE learns from expert demonstrations without access to actions, achieving strong performance guarantees.
problem Imitation Learning from Observations alone (ILFO) where actions are not observed.
method MobILE integrates optimism in the face of uncertainty into distribution matching IL framework, trading off exploration and imitation.
result MobILE provides strong performance guarantees for certain MDP dynamics and demonstrates efficacy on OpenAI Gym tasks.
Randomly guessing weights helps analyze RL benchmarks objectively.
problem Understanding the complexity of reinforcement learning benchmarks.
method Generate policy networks by randomly guessing their parameters, evaluate on benchmarks, and analyze results.
result Small untrained networks can provide a robust baseline for various RL tasks.
New algorithm learns policies from expert observations alone, efficiently.
problem Imitation Learning from expert observations in large-scale MDPs.
method Forward Adversarial Imitation Learning (FAIL) algorithm, minimizing IP metric between expert and learner observation distributions.
result First provably efficient algorithm in ILFO setting, learning near-optimal policies with polynomial sample complexity.
A novel RL objective and prioritization framework improve performance and sample-efficiency in multi-goal tasks.
problem Learning diverse goals in multi-goal reinforcement learning.
method Maximum entropy regularization for objective and prioritization framework.
result Promising improvements in performance and sample-efficiency on multi-goal robotic tasks.
Paper combines DQN and return-based RL for improved policy performance.
problem Improving policy performance in reinforcement learning.
method Integrates DQN and return-based reinforcement learning, introduces two measurements to quantify policy discrepancy.
result The proposed measurements accurately express trace coefficient and improve approximation to return.
Improved MCTS for asymmetric trees and loops.
problem Efficiency issues in MCTS for asymmetric trees and loops.
method Extended MCTS-T algorithm with modifications for asymmetric termination and loops, using a modified UCB formula.
result Significantly improved efficiency in asymmetric domains and loops compared to standard MCTS.
PettingZoo library accelerates multi-agent reinforcement learning research.
problem Challenges in multi-agent reinforcement learning, especially conceptual models of games.
method Developed PettingZoo library with AEC games model to address multi-agent reinforcement learning challenges.
result AEC games model addresses conceptual issues in multi-agent reinforcement learning environments.
IDAC improves reinforcement learning efficiency by modeling implicit distributions.
problem Improving sample efficiency in reinforcement learning algorithms.
method IDAC uses two DGNs for a distributional critic and a semi-implicit actor to model implicit policy distributions.
result IDAC outperforms state-of-the-art algorithms on OpenAI Gym environments.
DQN with model-based exploration improves learning in sparse reward environments.
problem Poor sample efficiency in sparse reward environments.
method Combines model-free and model-based approaches for better exploration.
result Improves performance in environments with sparse rewards.
Guiding reinforcement learning with suboptimal controllers speeds up training.
problem Sparse rewards in reinforcement learning make exploration inefficient.
method Use a suboptimal controller to guide exploration, applying a Q-filter loss conditionally.
result The approach leads to faster policy refinement and better performance.
RPSP networks combine PSRs and RNNs for reinforcement learning in POE.
problem Learning in partially observable environments.
method Recurrent filter with PSR, reactive policy, gradient descent.
result RPSP networks outperform memory-preserving models.
Dual memory improves reinforcement learning efficiency.
problem Training inefficiency in reinforcement learning.
method Introduces a dual memory structure with a main and cache memory.
result Dual memory structure leads to higher scores in reinforcement learning environments.
Paper proposes a new RL approach combining IL and RL methods to improve decision-making.
problem Challenges in RL with large state and action spaces, and difficulty in reward determination.
method Combines Imitation Learning and RL methods (SARSA and A3C) to learn sequential decision-making policies.
result Significantly decreases human effort and exploration time in learning decision-making policies.
A new framework reduces RL training cost by optimizing hyper-parameters.
problem High sampling cost in RL due to complex hyper-parameter tuning.
method Proposes a 'reinforcement on reinforcement' (RoR) architecture to decompose tasks into two layers of RL.
result The proposed framework achieves up to 56% expected sampling cost saving.
UCB-RS uses RS to improve UCB for online advertising.
problem Improving recommendation in online advertising.
method UCB-RS, combining UCB with recommendation system.
result UCB-RS outperforms other reinforcement learning methods in RecoGym.
POSA optimizes policy gradients by reducing variance with RB and CV.
problem High variance in policy gradient estimators for high-dimensional control tasks.
method POSA uses ASDG estimator combining RB and CV, learning action space factorization.
result POSA improves performance on high-dimensional control tasks.
MDP Playground tests RL agents across various dimensions for better understanding and debugging.
problem Understanding and debugging reinforcement learning agents across diverse environments and dimensions.
method Controlled testbed with adjustable dimensions for different RL challenges.
result Insights into agent performance and interaction with various dimensions.
New method reduces overestimation in actor-critic reinforcement learning.
problem Function approximation errors in actor-critic methods lead to suboptimal policies.
method Proposes novel mechanisms to minimize overestimation, including using the minimum value between critics and delaying policy updates.
result Outperforms state-of-the-art methods on OpenAI gym tasks.
New Zap Q-learning accelerates reinforcement learning with neural networks.
problem Accelerate convergence of reinforcement learning algorithms.
method Introduces a new framework for analysis of stochastic approximation algorithms, proving consistency under non-degeneracy assumption.
result Zap Q-learning with neural network function approximation converges quickly and is robust to function approximation architecture choice.
Paper introduces SALE for better state-action learning in RL.
problem Challenges in representation learning for low-level states in RL.
method Introduces SALE, a novel approach for learning embeddings of state-action interactions.
result TD7 algorithm significantly outperforms existing continuous control algorithms.
Estimation Error Correction in Deep Reinforcement Learning for Deterministic Actor-Critic Methodscs.LG
A novel Q-learning variant reduces underestimation bias in deep actor-critic methods for reinforcement learning.
problem Underestimation bias in deep actor-critic methods for reinforcement learning.
method Introduces a parameter-free Q-learning variant that combines maximum and minimum operators to bound value estimates.
result Improves state-of-the-art performance on OpenAI Gym tasks.
TorchBeast simplifies RL research in PyTorch.
problem Conducting scalable RL research with minimal programming knowledge.
method Pure-Python and C++ implementation for parallel training; OpenAI Gym interface.
result Performs on-par with IMPALA on Atari.
New method tackles RL with observational data, confounders.
problem Learning good policies from historical data with unobserved confounders.
method Extends Actor-Critic method to deconfounding variant.
result Proposed algorithms outperform traditional RL methods in confounded environments.
Deep RL model learns 2.5D fighting games with height ambiguity.
problem Ambiguity in character height/depth and sequential action orders in 2.5D fighting games.
method Modified A3C network with Recurrent Info network for combo skill observation.
result Successfully learned and played Little Fighter 2 (LF2) 2.5D fighting game.
Adapts deep reinforcement learning to ordinal rewards.
problem Using numerical rewards in reinforcement learning has drawbacks; ordinal rewards offer an alternative.
method Develops a general approach to converting reinforcement learning algorithms to ordinal reward systems, including Ordinal Deep Q-Networks.
result Ordinal Deep Q-Networks perform comparably to numerical variants on engineered problems and better on simpler reward signals.
New optimization algorithms on orthogonal group for machine learning.
problem Efficient optimization on the orthogonal group for machine learning tasks.
method Stochastic geometric algorithms on Lie groups.
result Strong performance on diverse machine learning tasks.
Proposes an amortized variational framework for Deep Q Networks.
problem Efficient exploration in deep reinforcement learning.
method Amortized variational inference for action value function approximation.
result Significantly less learning parameters and better performance.
Quantum variational circuits improve reinforcement learning efficiency.
problem Improving reinforcement learning algorithms using quantum computing.
method Investigation of quantum variational circuits for DQN and Double DQN, encoding classical data for quantum circuits.
result Quantum variational circuits can solve reinforcement learning tasks with a smaller parameter space.
ReF-ER algorithm improved performance in multi-agent reinforcement learning.
problem Improving performance in multi-agent reinforcement learning environments.
method Extended ReF-ER algorithm to include dependencies between agents and modeled environment dynamics.
result ReF-ER MARL outperforms state-of-the-art algorithms in collaborative environments.
This work learns an agent's better-designed body to improve policy learning.
problem Learning an agent's optimal design for a task is rarely optimized.
method Proposed an alteration to OpenAI Gym framework to allow joint learning of policy and agent design.
result An agent can learn a better body design that facilitates policy learning and reveals design principles.
SDPG algorithm improves sample efficiency and reward in DRL for continuous action spaces.
problem Improving sample efficiency and reward in distributional reinforcement learning for continuous action spaces.
method SDPG algorithm models return distribution using samples via reparameterization technique.
result SDPG shows better sample efficiency and higher reward in OpenAI Gym environments.
A method uses a supervisor policy to train a new policy safely and quickly for robots.
problem Training a new control policy safely and quickly for robots.
method Weighted average of supervisor and learned policies, pioneer network to compensate.
result Demonstrated effectiveness in OpenAI Gym experiments.
Robot predicts future frames for navigating dynamic environments using LSTM autoencoder.
problem Predicting movement of objects in dynamic environments with moving obstacles.
method Multi-layer LSTM autoencoder network that reconstructs future frames conditioned on the agent's action.
result The proposed network generates future frames that can be used by reinforcement learning for navigation.
GRAC improves reinforcement learning by self-guiding and self-regularizing.
problem Learning divergence and slow updates in reinforcement learning algorithms.
method Self-regularized TD-learning and self-guided policy improvement.
result Achieved or outperformed state-of-the-art results on OpenAI gym tasks.