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Reinforcement Learning (RL)

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보존된 평가 기록: 3

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/google-research/google-research/tree/master/pse

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Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning

https://github.com/google-research/google-research/tree/master/rce

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Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification

https://github.com/google-research/google-research/tree/master/rl_metrics_aaai2021

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Metrics and continuity in reinforcement learning

https://github.com/google-research/google-research/tree/master/social_rl

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Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design

https://github.com/google-research/google-research/tree/master/xirl

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XIRL: Cross-embodiment Inverse Reinforcement Learning

https://github.com/google-research/look-back-when-surprised

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Introspective Experience Replay: Look Back When Surprised

https://github.com/google-research/realworldrl_suite

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An empirical investigation of the challenges of real-world reinforcement learning

https://github.com/google-research/recsim

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RecSim: A Configurable Simulation Platform for Recommender Systems

https://github.com/google-research/reincarnating_rl

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Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress

https://github.com/google-research/rigl/tree/master/rigl/rl

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The State of Sparse Training in Deep Reinforcement Learning

https://github.com/google-research/rl-reliability-metrics

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Measuring the Reliability of Reinforcement Learning Algorithms

https://github.com/google-research/rlds

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RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning

https://github.com/google-research/rliable

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Deep Reinforcement Learning at the Edge of the Statistical Precipice

https://github.com/google-research/tabnas

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TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets

https://github.com/google-research/tf-opt

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Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing

https://github.com/google-research/valan

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VALAN: Vision and Language Agent Navigation

https://github.com/google-research/world_models

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Models, Pixels, and Rewards: Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning

https://github.com/google/brain_autorl

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Evolving Reinforcement Learning Algorithms

https://github.com/google/brain_autorl/tree/main/evolving_rl

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Evolving Reinforcement Learning Algorithms

https://github.com/google/brax

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Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation

https://github.com/google/dopamine

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Deep Reinforcement Learning

https://github.com/gorkamunoz/rl_opts

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Optimal foraging strategies can be learned

https://github.com/gouqi666/rast

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Diversify Question Generation with Retrieval-Augmented Style Transfer

https://github.com/gouxiangchen/SILCR

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Self-Imitation Learning for Robot Tasks with Sparse and Delayed Rewards

https://github.com/gpoesia/socratic-tutor

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Contrastive Reinforcement Learning of Symbolic Reasoning Domains

https://github.com/gpt4vision/r1-sgg

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Compile Scene Graphs with Reinforcement Learning

https://github.com/grcmupc/portrait

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On the Implementation of a Reinforcement Learning-based Capacity Sharing Algorithm in O-RAN

https://github.com/greydanus/visualize_atari

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Visualizing and Understanding Atari Agents

https://github.com/grockious/deepsynth

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DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning

https://github.com/grockious/lcrl

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Logically-Constrained Reinforcement Learning

https://github.com/gsastry/human-rl

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Trial without Error: Towards Safe Reinforcement Learning via Human Intervention

https://github.com/gsbDBI/contextual_bandits_evaluation

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Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Bandits

https://github.com/gsoumyajit/Finite-Horizon-with-constraints

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A policy gradient approach for Finite Horizon Constrained Markov Decision Processes

https://github.com/gt-star-lab/constrained-rl-dexterous-manipulation

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Constrained Reinforcement Learning for Dexterous Manipulation

https://github.com/gtxygyzb/saturn-code

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SATURN: SAT-based Reinforcement Learning to Unleash Language Model Reasoning

https://github.com/guanxinglu/vlarl

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VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning

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