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Improvements/Bug Fixes

Misc

PR #131

• fix overflow error in np.exp of SoftmaxPolicy, BoltzmannPolicy by casting to float64 instead of float32
• improve overall np.isfinite asserts
• remove index after reset in *analysis.csv
• remove unused specs
• reorganize and expand test specs
• guard continuous action value range in continuous policies
• fix analytics param variable sourcing

DDPG

PR: #131

• add EpsilonGreedyNoisePolicy

PER

PR: #131

• add memory.update(errors) throughout all agents
• add shape assert for Q values and errors throughout
• auto max_mem_len as max_timestep * max_epis/3 if not specified
• put the missing abs for init reward

New Algorithms

ActorCritic

PR: #118

• add ActorCritic agent
• add its policies, Discrete: ArgmaxPolicy, SoftmaxPolicy; Continuous: BoundedPolicy, GaussianPolicy
• add basic specs, solve Cartpole-v0, Cartpole-v1, yet to solve the others

DDPG

PR: #118

• add DDPG agent with custom tensorflow ops
• add its policies (only Continuous now): NoNoisePolicy, LinearNoisePolicy, GaussianWhiteNoisePolicy, OUNoisePolicy
• add basic specs, solve Pendulum-v0

Improvements/Bug Fixes

PR: #118

• use logger.warn instead of raise error when component locks are violated
• fix #114, #115 matplotlib backend setting issue. now single trial will live-plot and render
• mute DoubleDQN as it breaks; instead revert to the single-model recompile from DQN

Component Locks

PR: #120

We have a lot of components, and not all of them are compatible with another. When scheduling experiments and designing specs it is hard to keep all of them in check. This adds a component locks that does automatic checking of all specs when importing, by using the specified locks in rl/spec/component_locks.json. Uses the minimum description length design principle. When adding new components, be sure to update this file.

• add double-network component lock
• add discrete-action component lock; assume continuous agent can handle discrete action spaces as a generalization

Improved Installation

PR: #121 Solves: #113, #114, #115

• fix broken gym installation. See gym PR 558
• layout installation steps in doc, use binaries for server setup
• introduce version lock for dependencies with requirements.txt, environment.yml
• support installation by system python, virtualenv, conda, integrate into Grunt
• add quickstart_dqn for example quickstart in doc

Bug Fixes

DoubleDQN

PR: #119

• restore missing recompile_model call to the second model in DoubleDQN.

Bug Fixes

BoltzmannPolicy

PR: #109

• fix state reshape with dimension > 1 using np.expand_dims
• guard underflow by doing np.clip before np.exp

Misc

• rename class from DoubleDQNPolicy to DoubleDQNEpsilonGreedyPolicy for clarity
• refactor useless RENDER key from rl/spec/problems.json into rl/experiment.py

Bug Fixes

PER

PR: #108

• fix PER breakage on negative error = reward by adding a bump min_priority = abs(10 * SOLVED_MEAN_REWARD)
• add a positive min_priority for all problems since they may have negative rewards. We cannot do error = abs(reward) because it is sign sensitive for priority calculation
• add assert guard to ensure priority is not nan

First stable release of OpenAI Lab

PR: #106

• stable and generalized RL components design
• implement discrete agents: DQN, double-DQN, SARSA, PER
• run dozens of experiments as Lab tests. Solve numerous discrete environments on Fitness Matrix, with PR submissions. Mainly CartPole-v0, CartPole-v1, Acrobot-v1, LunarLander-v2
• complete documentation page
• complete analytics framework and generalized fitness_score as evaluation metrics
• stable system design after many iterations
• ready for more implementations and new research